| """ |
| K R&D Lab — Learning Playground |
| Author: Oksana Kolisnyk | kosatiks-group.pp.ua |
| Repo: github.com/TEZv/K-RnD-Lab-PHYLO-03_2026 |
| """ |
|
|
| import gradio as gr |
| import pandas as pd |
| import numpy as np |
| import json |
| import re |
| import csv |
| import os |
| import time |
| import math |
| import hashlib |
| import datetime |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from io import BytesIO |
| from PIL import Image |
| from pathlib import Path |
| from datetime import datetime |
| from functools import wraps |
| import plotly.graph_objects as go |
| import plotly.express as px |
| import tabulate |
|
|
| |
| from journal import journal_log, journal_read, clear_journal, JOURNAL_CATEGORIES |
| from chatbot import build_chatbot_tab |
|
|
| |
| print("Gradio version:", gr.__version__) |
| print("Starting app...") |
|
|
| |
| BG = "#0f172a" |
| CARD = "#1e293b" |
| ACC = "#f97316" |
| ACC2 = "#38bdf8" |
| TXT = "#f1f5f9" |
| GRN = "#22c55e" |
| RED = "#ef4444" |
| DIM = "#8e9bae" |
| BORDER = "#334155" |
|
|
| |
| |
| |
| CACHE_DIR = "./cache" |
| os.makedirs(CACHE_DIR, exist_ok=True) |
| CACHE_TTL = 86400 |
|
|
| def _cache_key(endpoint: str, query: str) -> str: |
| raw = f"{endpoint}_{query}" |
| return hashlib.md5(raw.encode()).hexdigest() |
|
|
| def cache_get(endpoint: str, query: str): |
| key = _cache_key(endpoint, query) |
| path = os.path.join(CACHE_DIR, f"{endpoint}_{key}.json") |
| if os.path.exists(path): |
| mtime = os.path.getmtime(path) |
| if time.time() - mtime < CACHE_TTL: |
| with open(path) as f: |
| return json.load(f) |
| return None |
|
|
| def cache_set(endpoint: str, query: str, data): |
| key = _cache_key(endpoint, query) |
| path = os.path.join(CACHE_DIR, f"{endpoint}_{key}.json") |
| with open(path, "w") as f: |
| json.dump(data, f) |
|
|
| |
| |
| |
| def retry(max_attempts=3, delay=1): |
| def decorator(func): |
| @wraps(func) |
| def wrapper(*args, **kwargs): |
| for attempt in range(max_attempts): |
| try: |
| return func(*args, **kwargs) |
| except Exception as e: |
| print(f"Attempt {attempt+1} failed for {func.__name__}: {e}") |
| if attempt == max_attempts - 1: |
| raise |
| time.sleep(delay * (attempt + 1)) |
| return None |
| return wrapper |
| return decorator |
|
|
| |
| MIRNA_DB = { |
| "BRCA2": [ |
| {"miRNA":"hsa-miR-148a-3p","log2FC":-0.70,"padj":0.013,"targets":"DNMT1, AKT2","pathway":"Epigenetic reprogramming"}, |
| {"miRNA":"hsa-miR-30e-5p","log2FC":-0.49,"padj":0.032,"targets":"MYC, KRAS","pathway":"Oncogene suppression"}, |
| {"miRNA":"hsa-miR-551b-3p","log2FC":-0.59,"padj":0.048,"targets":"SMAD4, CDK6","pathway":"TGF-beta / CDK4/6"}, |
| {"miRNA":"hsa-miR-22-3p","log2FC":-0.43,"padj":0.041,"targets":"HIF1A, PTEN","pathway":"Hypoxia / PI3K"}, |
| {"miRNA":"hsa-miR-200c-3p","log2FC":-0.38,"padj":0.044,"targets":"ZEB1, ZEB2","pathway":"EMT suppression"}, |
| ], |
| "BRCA1": [ |
| {"miRNA":"hsa-miR-155-5p","log2FC":-0.81,"padj":0.008,"targets":"SHIP1, SOCS1","pathway":"Immune evasion"}, |
| {"miRNA":"hsa-miR-146a-5p","log2FC":-0.65,"padj":0.019,"targets":"TRAF6, IRAK1","pathway":"NF-kB signalling"}, |
| {"miRNA":"hsa-miR-21-5p","log2FC":-0.55,"padj":0.027,"targets":"PTEN, PDCD4","pathway":"Apoptosis"}, |
| {"miRNA":"hsa-miR-17-5p","log2FC":-0.47,"padj":0.036,"targets":"RB1, E2F1","pathway":"Cell cycle"}, |
| {"miRNA":"hsa-miR-34a-5p","log2FC":-0.41,"padj":0.049,"targets":"BCL2, CDK6","pathway":"p53 axis"}, |
| ], |
| "TP53": [ |
| {"miRNA":"hsa-miR-34a-5p","log2FC":-1.10,"padj":0.001,"targets":"BCL2, CDK6","pathway":"p53-miR-34 axis"}, |
| {"miRNA":"hsa-miR-192-5p","log2FC":-0.90,"padj":0.005,"targets":"MDM2, DHFR","pathway":"p53 feedback"}, |
| {"miRNA":"hsa-miR-145-5p","log2FC":-0.75,"padj":0.012,"targets":"MYC, EGFR","pathway":"Growth suppression"}, |
| {"miRNA":"hsa-miR-107","log2FC":-0.62,"padj":0.023,"targets":"CDK6, HIF1B","pathway":"Hypoxia / cell cycle"}, |
| {"miRNA":"hsa-miR-215-5p","log2FC":-0.51,"padj":0.038,"targets":"DTL, DHFR","pathway":"DNA damage response"}, |
| ], |
| } |
| SIRNA_DB = { |
| "LUAD": [ |
| {"Gene":"SPC24","dCERES":-0.175,"log2FC":1.13,"Drug_status":"Novel","siRNA":"GCAGCUGAAGAAACUGAAU"}, |
| {"Gene":"BUB1B","dCERES":-0.119,"log2FC":1.12,"Drug_status":"Novel","siRNA":"CCAAAGAGCUGAAGAACAU"}, |
| {"Gene":"CDC45","dCERES":-0.144,"log2FC":1.26,"Drug_status":"Novel","siRNA":"GCAUCAAGAUGAAGGAGAU"}, |
| {"Gene":"PLK1","dCERES":-0.239,"log2FC":1.03,"Drug_status":"Clinical","siRNA":"GACGCUCAAGAUGCAGAUU"}, |
| {"Gene":"CDK1","dCERES":-0.201,"log2FC":1.00,"Drug_status":"Clinical","siRNA":"GCAGAAGCACUGAAGAUUU"}, |
| ], |
| "BRCA": [ |
| {"Gene":"AURKA","dCERES":-0.165,"log2FC":1.20,"Drug_status":"Clinical","siRNA":"GCACUGAAGAUGCAGAAUU"}, |
| {"Gene":"AURKB","dCERES":-0.140,"log2FC":1.15,"Drug_status":"Clinical","siRNA":"CCUGAAGACGCUCAAGGUU"}, |
| {"Gene":"CENPW","dCERES":-0.125,"log2FC":0.95,"Drug_status":"Novel","siRNA":"GCAGAAGCACUGAAGAUUU"}, |
| {"Gene":"RFC2","dCERES":-0.136,"log2FC":0.50,"Drug_status":"Novel","siRNA":"GCAAGAUGCAGAAGCACUU"}, |
| {"Gene":"TYMS","dCERES":-0.131,"log2FC":0.72,"Drug_status":"Approved","siRNA":"GGACGCUCAAGAUGCAGAU"}, |
| ], |
| "COAD": [ |
| {"Gene":"KRAS","dCERES":-0.210,"log2FC":0.80,"Drug_status":"Clinical","siRNA":"GCUGGAGCUGGUGGUAGUU"}, |
| {"Gene":"WEE1","dCERES":-0.180,"log2FC":1.05,"Drug_status":"Clinical","siRNA":"GCAGCUGAAGAAACUGAAU"}, |
| {"Gene":"CHEK1","dCERES":-0.155,"log2FC":0.90,"Drug_status":"Clinical","siRNA":"CCAAAGAGCUGAAGAACAU"}, |
| {"Gene":"RFC2","dCERES":-0.130,"log2FC":0.55,"Drug_status":"Novel","siRNA":"GCAUCAAGAUGAAGGAGAU"}, |
| {"Gene":"PKMYT1","dCERES":-0.122,"log2FC":1.07,"Drug_status":"Clinical","siRNA":"GACGCUCAAGAUGCAGAUU"}, |
| ], |
| } |
| CERNA = [ |
| {"lncRNA":"CYTOR","miRNA":"hsa-miR-138-5p","target":"AKT1","pathway":"TREM2 core signaling"}, |
| {"lncRNA":"CYTOR","miRNA":"hsa-miR-138-5p","target":"NFKB1","pathway":"Neuroinflammation"}, |
| {"lncRNA":"GAS5","miRNA":"hsa-miR-21-5p","target":"PTEN","pathway":"Neuroinflammation"}, |
| {"lncRNA":"GAS5","miRNA":"hsa-miR-222-3p","target":"IL1B","pathway":"Neuroinflammation"}, |
| {"lncRNA":"HOTAIRM1","miRNA":"hsa-miR-9-5p","target":"TREM2","pathway":"Direct TREM2 regulation"}, |
| ] |
| ASO = [ |
| {"lncRNA":"GAS5","position":119,"accessibility":0.653,"GC_pct":50,"Tm":47.2,"priority":"HIGH"}, |
| {"lncRNA":"CYTOR","position":507,"accessibility":0.653,"GC_pct":50,"Tm":46.8,"priority":"HIGH"}, |
| {"lncRNA":"HOTAIRM1","position":234,"accessibility":0.621,"GC_pct":44,"Tm":44.1,"priority":"MEDIUM"}, |
| {"lncRNA":"LINC00847","position":89,"accessibility":0.598,"GC_pct":56,"Tm":48.3,"priority":"MEDIUM"}, |
| {"lncRNA":"ZFAS1","position":312,"accessibility":0.571,"GC_pct":48,"Tm":45.5,"priority":"MEDIUM"}, |
| ] |
| FGFR3 = { |
| "P1 (hairpin loop)": [ |
| {"Compound":"CHEMBL1575701","RNA_score":0.809,"Toxicity":0.01,"Final_score":0.793}, |
| {"Compound":"CHEMBL15727","RNA_score":0.805,"Toxicity":0.00,"Final_score":0.789}, |
| {"Compound":"Thioguanine","RNA_score":0.888,"Toxicity":32.5,"Final_score":0.742}, |
| {"Compound":"Deazaguanine","RNA_score":0.888,"Toxicity":35.0,"Final_score":0.735}, |
| {"Compound":"CHEMBL441","RNA_score":0.775,"Toxicity":5.2,"Final_score":0.721}, |
| ], |
| "P10 (G-quadruplex)": [ |
| {"Compound":"CHEMBL15727","RNA_score":0.805,"Toxicity":0.00,"Final_score":0.789}, |
| {"Compound":"CHEMBL5411515","RNA_score":0.945,"Toxicity":37.1,"Final_score":0.761}, |
| {"Compound":"CHEMBL90","RNA_score":0.760,"Toxicity":2.1,"Final_score":0.745}, |
| {"Compound":"CHEMBL102","RNA_score":0.748,"Toxicity":8.4,"Final_score":0.712}, |
| {"Compound":"Berberine","RNA_score":0.735,"Toxicity":3.2,"Final_score":0.708}, |
| ], |
| } |
| VARIANT_DB = { |
| "BRCA1:p.R1699Q": {"score":0.03,"cls":"Benign","conf":"High"}, |
| "BRCA1:p.R1699W": {"score":0.97,"cls":"Pathogenic","conf":"High"}, |
| "BRCA2:p.D2723A": {"score":0.999,"cls":"Pathogenic","conf":"High"}, |
| "TP53:p.R248W": {"score":0.998,"cls":"Pathogenic","conf":"High"}, |
| "TP53:p.R248Q": {"score":0.995,"cls":"Pathogenic","conf":"High"}, |
| "EGFR:p.L858R": {"score":0.96,"cls":"Pathogenic","conf":"High"}, |
| "ALK:p.F1174L": {"score":0.94,"cls":"Pathogenic","conf":"High"}, |
| } |
| PLAIN = { |
| "Pathogenic": "This variant is likely to cause disease. Clinical follow-up is strongly recommended.", |
| "Likely Pathogenic": "This variant is probably harmful. Discuss with your doctor.", |
| "Benign": "This variant is likely harmless. Common in the general population.", |
| "Likely Benign": "This variant is probably harmless. No strong reason for concern.", |
| } |
| BM_W = { |
| "CTHRC1":0.18,"FHL2":0.15,"LDHA":0.14,"P4HA1":0.13, |
| "SERPINH1":0.12,"ABCA8":-0.11,"CA4":-0.10,"CKB":-0.09, |
| "NNMT":0.08,"CACNA2D2":-0.07 |
| } |
| PROTEINS = ["albumin","apolipoprotein","fibrinogen","vitronectin", |
| "clusterin","igm","iga","igg","complement","transferrin", |
| "alpha-2-macroglobulin"] |
|
|
| |
| PROJECT_CODES = [ |
| "S1-A·R1a", "S1-A·R1b", "S1-A·R2e", |
| "S1-B·R1a", "S1-B·R2a", "S1-B·R3a", "S1-B·R3b", |
| "S1-C·R1a", "S1-C·R1b", "S1-C·R2a", |
| "S1-D·R1a", "S1-D·R2a", "S1-D·R3a", "S1-D·R4a", "S1-D·R5a", |
| "S1-E·R1a", "S1-E·R1b", |
| "S1-F·R1a", "S1-F·R2a", "S1-F·R3a", |
| "S1-G·General" |
| ] |
|
|
| |
| DIPG_VARIANTS = [ |
| {"Variant":"H3K27M (H3F3A)","Freq_pct":78,"Pathway":"PRC2 inhibition → global H3K27me3 loss","Drug_status":"ONC201 (clinical)","Circadian_gene":"BMAL1 suppressed"}, |
| {"Variant":"ACVR1 p.R206H","Freq_pct":21,"Pathway":"BMP/SMAD hyperactivation","Drug_status":"LDN-193189 (preclinical)","Circadian_gene":"PER1 disrupted"}, |
| {"Variant":"PIK3CA p.H1047R","Freq_pct":15,"Pathway":"PI3K/AKT/mTOR","Drug_status":"Copanlisib (clinical)","Circadian_gene":"CRY1 altered"}, |
| {"Variant":"TP53 p.R248W","Freq_pct":14,"Pathway":"DNA damage response loss","Drug_status":"APR-246 (clinical)","Circadian_gene":"p53-CLOCK axis"}, |
| {"Variant":"PDGFRA amp","Freq_pct":13,"Pathway":"RTK/RAS signalling","Drug_status":"Avapritinib (clinical)","Circadian_gene":"REV-ERB altered"}, |
| ] |
| DIPG_CSF_LNP = [ |
| {"Formulation":"MC3-DSPC-Chol-PEG","Size_nm":92,"Zeta_mV":-4.1,"CSF_protein":"Beta2-microglobulin","ApoE_pct":12.4,"BBB_est":0.41,"Priority":"HIGH"}, |
| {"Formulation":"DLin-KC2-DSPE-PEG","Size_nm":87,"Zeta_mV":-3.8,"CSF_protein":"Cystatin C","ApoE_pct":14.1,"BBB_est":0.47,"Priority":"HIGH"}, |
| {"Formulation":"C12-200-DOPE-PEG","Size_nm":103,"Zeta_mV":-5.2,"CSF_protein":"Albumin (low)","ApoE_pct":9.8,"BBB_est":0.33,"Priority":"MEDIUM"}, |
| {"Formulation":"DODAP-DSPC-Chol","Size_nm":118,"Zeta_mV":-2.1,"CSF_protein":"Transferrin","ApoE_pct":7.2,"BBB_est":0.24,"Priority":"LOW"}, |
| ] |
|
|
| UVM_VARIANTS = [ |
| {"Variant":"GNAQ p.Q209L","Freq_pct":46,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"METTL3 upregulated"}, |
| {"Variant":"GNA11 p.Q209L","Freq_pct":32,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"WTAP upregulated"}, |
| {"Variant":"BAP1 loss","Freq_pct":47,"Pathway":"Chromatin remodeling → metastasis","Drug_status":"No approved (HDAC trials)","m6A_writer":"FTO overexpressed"}, |
| {"Variant":"SF3B1 p.R625H","Freq_pct":19,"Pathway":"Splicing alteration → neoepitopes","Drug_status":"H3B-8800 (clinical)","m6A_writer":"METTL14 altered"}, |
| {"Variant":"EIF1AX p.A113_splice","Freq_pct":14,"Pathway":"Translation initiation","Drug_status":"Novel — no drug","m6A_writer":"YTHDF2 suppressed"}, |
| ] |
| UVM_VITREOUS_LNP = [ |
| {"Formulation":"SM-102-DSPC-Chol-PEG","Vitreal_protein":"Hyaluronan-binding","Size_nm":95,"Zeta_mV":-3.2,"Retention_h":18,"Priority":"HIGH"}, |
| {"Formulation":"Lipid-H-DOPE-PEG","Vitreal_protein":"Vitronectin dominant","Size_nm":88,"Zeta_mV":-4.0,"Retention_h":22,"Priority":"HIGH"}, |
| {"Formulation":"DOTAP-DSPC-PEG","Vitreal_protein":"Albumin wash-out","Size_nm":112,"Zeta_mV":+2.1,"Retention_h":6,"Priority":"LOW"}, |
| {"Formulation":"MC3-DPPC-Chol","Vitreal_protein":"Clusterin-rich","Size_nm":101,"Zeta_mV":-2.8,"Retention_h":14,"Priority":"MEDIUM"}, |
| ] |
|
|
| PAML_VARIANTS = [ |
| {"Variant":"FLT3-ITD","Freq_pct":25,"Pathway":"RTK constitutive activation → JAK/STAT","Drug_status":"Midostaurin (approved)","Ferroptosis":"GPX4 suppressed"}, |
| {"Variant":"NPM1 c.860_863dupTCAG","Freq_pct":30,"Pathway":"Nuclear export deregulation","Drug_status":"APR-548 combo (clinical)","Ferroptosis":"SLC7A11 upregulated"}, |
| {"Variant":"DNMT3A p.R882H","Freq_pct":18,"Pathway":"Epigenetic dysregulation","Drug_status":"Azacitidine (approved)","Ferroptosis":"ACSL4 altered"}, |
| {"Variant":"CEBPA biallelic","Freq_pct":8,"Pathway":"Myeloid differentiation block","Drug_status":"Novel target","Ferroptosis":"NRF2 pathway"}, |
| {"Variant":"IDH1/2 mutation","Freq_pct":15,"Pathway":"2-HG oncometabolite → TET2 inhibition","Drug_status":"Enasidenib (approved)","Ferroptosis":"Iron metabolism disrupted"}, |
| ] |
| PAML_BM_LNP = [ |
| {"Formulation":"ALC-0315-DSPC-Chol-PEG","BM_protein":"ApoE + Clusterin","Size_nm":98,"Zeta_mV":-3.5,"Marrow_uptake_pct":34,"Priority":"HIGH"}, |
| {"Formulation":"MC3-DOPE-Chol-PEG","BM_protein":"Fibronectin dominant","Size_nm":105,"Zeta_mV":-4.2,"Marrow_uptake_pct":28,"Priority":"HIGH"}, |
| {"Formulation":"DLin-MC3-DPPC","BM_protein":"Vitronectin-rich","Size_nm":91,"Zeta_mV":-2.9,"Marrow_uptake_pct":19,"Priority":"MEDIUM"}, |
| {"Formulation":"Cationic-DOTAP-Chol","BM_protein":"Opsonin-heavy","Size_nm":132,"Zeta_mV":+8.1,"Marrow_uptake_pct":8,"Priority":"LOW"}, |
| ] |
|
|
| |
| def safe_img_from_fig(fig): |
| """Convert matplotlib figure to PIL Image safely.""" |
| try: |
| buf = BytesIO() |
| fig.savefig(buf, format="png", dpi=120, facecolor=CARD) |
| buf.seek(0) |
| img = Image.open(buf) |
| plt.close(fig) |
| return img |
| except Exception: |
| plt.close(fig) |
| return Image.new('RGB', (100, 100), color=CARD) |
|
|
| |
| def predict_mirna(gene): |
| df = pd.DataFrame(MIRNA_DB.get(gene, [])) |
| journal_log("S1-B·R1a", gene, f"{len(df)} miRNAs") |
| return df |
|
|
| def predict_sirna(cancer): |
| df = pd.DataFrame(SIRNA_DB.get(cancer, [])) |
| journal_log("S1-B·R2a", cancer, f"{len(df)} targets") |
| return df |
|
|
| def get_lncrna(): |
| journal_log("S1-B·R3a", "load", "ceRNA") |
| return pd.DataFrame(CERNA) |
|
|
| def get_aso(): |
| journal_log("S1-B·R3b", "load", "ASO") |
| return pd.DataFrame(ASO) |
|
|
| def predict_drug(pocket): |
| try: |
| df = pd.DataFrame(FGFR3.get(pocket, [])) |
| if df.empty: |
| return pd.DataFrame(), None |
| fig, ax = plt.subplots(figsize=(6, 4), facecolor=CARD) |
| ax.set_facecolor(CARD) |
| ax.barh(df["Compound"], df["Final_score"], color=ACC) |
| ax.set_xlabel("Final Score", color=TXT) |
| ax.tick_params(colors=TXT) |
| for sp in ax.spines.values(): sp.set_edgecolor(BORDER) |
| ax.set_title(f"Top compounds — {pocket}", color=TXT, fontsize=10) |
| plt.tight_layout() |
| img = safe_img_from_fig(fig) |
| journal_log("S1-C·R1a", pocket, f"Top: {df.iloc[0]['Compound'] if len(df) else 'none'}") |
| return df, img |
| except Exception as e: |
| journal_log("S1-C·R1a", pocket, f"Error: {str(e)}") |
| return pd.DataFrame(), None |
|
|
| def predict_variant(hgvs, sift, polyphen, gnomad): |
| hgvs = hgvs.strip() |
| if hgvs in VARIANT_DB: |
| r = VARIANT_DB[hgvs]; cls, conf, score = r["cls"], r["conf"], r["score"] |
| else: |
| score = 0.0 |
| if sift < 0.05: score += 0.4 |
| if polyphen > 0.85: score += 0.35 |
| if gnomad < 0.0001: score += 0.25 |
| score = round(score, 3) |
| cls = "Pathogenic" if score > 0.6 else "Likely Pathogenic" if score > 0.4 else "Benign" |
| conf = "High" if (sift < 0.01 or sift > 0.9) else "Moderate" |
| colour = RED if "Pathogenic" in cls else GRN |
| icon = "⚠️ WARNING" if "Pathogenic" in cls else "✅ OK" |
| journal_log("S1-A·R1a", hgvs or f"SIFT={sift}", f"{cls} score={score}") |
| return ( |
| f"<div style=\'background:{CARD};padding:16px;border-radius:8px;font-family:sans-serif;color:{TXT}\'>" |
| f"<p style=\'font-size:11px;color:{DIM};margin:0 0 8px\'>S1-A·R1a · OpenVariant</p>" |
| f"<h3 style=\'color:{colour};margin:0 0 8px\'>{icon} {cls}</h3>" |
| f"<p>Score: <b>{score:.3f}</b> | Confidence: <b>{conf}</b></p>" |
| f"<div style=\'background:{BORDER};border-radius:4px;height:14px\'>" |
| f"<div style=\'background:{colour};height:14px;border-radius:4px;width:{int(score*100)}%\'></div></div>" |
| f"<p style=\'margin-top:12px\'>{PLAIN.get(cls,'')}</p>" |
| f"<p style=\'font-size:11px;color:{DIM}\'>Research only. Not clinical advice.</p></div>" |
| ) |
|
|
| def predict_corona(size, zeta, peg, lipid): |
| try: |
| score = 0 |
| if lipid == "Ionizable": score += 2 |
| elif lipid == "Cationic": score += 1 |
| if abs(zeta) < 10: score += 1 |
| if peg > 1.5: score += 2 |
| if size < 100: score += 1 |
| dominant = ["ApoE","Albumin","Fibrinogen","Vitronectin","ApoA-I"][min(score, 4)] |
| efficacy = "High" if score >= 4 else "Medium" if score >= 2 else "Low" |
| journal_log("S1-D·R1a", f"size={size},peg={peg},lipid={lipid}", f"dominant={dominant}") |
| return f"**Dominant corona protein:** {dominant}\n\n**Predicted efficacy:** {efficacy}\n\n**Score:** {score}/6" |
| except Exception as e: |
| return f"Error: {str(e)}" |
|
|
| def predict_cancer(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10): |
| try: |
| vals = [c1,c2,c3,c4,c5,c6,c7,c8,c9,c10] |
| names, weights = list(BM_W.keys()), list(BM_W.values()) |
| raw = sum(v*w for v,w in zip(vals, weights)) |
| prob = 1 / (1 + np.exp(-raw * 2)) |
| label = "CANCER" if prob > 0.5 else "HEALTHY" |
| colour = RED if prob > 0.5 else GRN |
| contribs = [v*w for v,w in zip(vals, weights)] |
| fig, ax = plt.subplots(figsize=(6, 3.5), facecolor=CARD) |
| ax.set_facecolor(CARD) |
| ax.barh(names, contribs, color=[ACC if c > 0 else ACC2 for c in contribs]) |
| ax.axvline(0, color=TXT, linewidth=0.8) |
| ax.set_xlabel("Contribution to cancer score", color=TXT) |
| ax.tick_params(colors=TXT, labelsize=8) |
| for sp in ax.spines.values(): sp.set_edgecolor(BORDER) |
| ax.set_title("Protein contributions", color=TXT, fontsize=10) |
| plt.tight_layout() |
| img = safe_img_from_fig(fig) |
| journal_log("S1-E·R1a", f"CTHRC1={c1},FHL2={c2}", f"{label} {prob:.2f}") |
| html_out = ( |
| f"<div style=\'background:{CARD};padding:14px;border-radius:8px;font-family:sans-serif;\'>" |
| f"<p style=\'font-size:11px;color:{DIM};margin:0 0 6px\'>S1-E·R1a · Liquid Biopsy</p>" |
| f"<span style=\'color:{colour};font-size:24px;font-weight:bold\'>{label}</span><br>" |
| f"<span style=\'color:{TXT};font-size:14px\'>Probability: {prob:.2f}</span></div>" |
| ) |
| return html_out, img |
| except Exception as e: |
| return f"<div style='color:{RED}'>Error: {str(e)}</div>", None |
|
|
| def predict_flow(size, zeta, peg, charge, flow_rate): |
| try: |
| csi = round(min((flow_rate/40)*0.6 + (peg/5)*0.2 + (1 if charge=="Cationic" else 0)*0.2, 1.0), 3) |
| stability = "High remodeling" if csi > 0.6 else "Medium" if csi > 0.3 else "Stable" |
| t = np.linspace(0, 60, 200) |
| kf, ks = 0.03*(1+flow_rate/40), 0.038*(1+flow_rate/40) |
| fig, ax = plt.subplots(figsize=(6, 3.5), facecolor=CARD) |
| ax.set_facecolor(CARD) |
| ax.plot(t, 60*np.exp(-0.03*t)+20, color="#60a5fa", ls="--", label="Albumin (static)") |
| ax.plot(t, 60*np.exp(-kf*t)+10, color="#60a5fa", label="Albumin (flow)") |
| ax.plot(t, 14*(1-np.exp(-0.038*t))+5, color=ACC, ls="--", label="ApoE (static)") |
| ax.plot(t, 20*(1-np.exp(-ks*t))+5, color=ACC, label="ApoE (flow)") |
| ax.set_xlabel("Time (min)", color=TXT); ax.set_ylabel("% Corona", color=TXT) |
| ax.tick_params(colors=TXT); ax.legend(fontsize=7, labelcolor=TXT, facecolor=CARD) |
| for sp in ax.spines.values(): sp.set_edgecolor(BORDER) |
| ax.set_title("Vroman Effect — flow vs static", color=TXT, fontsize=9) |
| plt.tight_layout() |
| img = safe_img_from_fig(fig) |
| journal_log("S1-D·R2a", f"flow={flow_rate}", f"CSI={csi}") |
| return f"**Corona Shift Index: {csi}** — {stability}", img |
| except Exception as e: |
| return f"Error: {str(e)}", None |
|
|
| def predict_bbb(smiles, pka, zeta): |
| try: |
| logp = smiles.count("C")*0.3 - smiles.count("O")*0.5 + 1.5 |
| apoe_pct = max(0, min(40, (7.0-pka)*8 + abs(zeta)*0.5 + logp*0.8)) |
| bbb_prob = min(0.95, apoe_pct/30) |
| tier = "HIGH (>20%)" if apoe_pct > 20 else "MEDIUM (10-20%)" if apoe_pct > 10 else "LOW (<10%)" |
| cats = ["ApoE%","BBB","logP","pKa fit","Zeta"] |
| vals = [apoe_pct/40, bbb_prob, min(logp/5,1), (7-abs(pka-6.5))/7, (10-abs(zeta))/10] |
| angles = np.linspace(0, 2*np.pi, len(cats), endpoint=False).tolist() |
| v2, a2 = vals+[vals[0]], angles+[angles[0]] |
| fig, ax = plt.subplots(figsize=(5, 4), subplot_kw={"polar":True}, facecolor=CARD) |
| ax.set_facecolor(CARD) |
| ax.plot(a2, v2, color=ACC, linewidth=2); ax.fill(a2, v2, color=ACC, alpha=0.2) |
| ax.set_xticks(angles); ax.set_xticklabels(cats, color=TXT, fontsize=8) |
| ax.tick_params(colors=TXT) |
| plt.tight_layout() |
| img = safe_img_from_fig(fig) |
| journal_log("S1-D·R3a", f"pka={pka},zeta={zeta}", f"ApoE={apoe_pct:.1f}%") |
| return f"**Predicted ApoE:** {apoe_pct:.1f}% — {tier}\n\n**BBB Probability:** {bbb_prob:.2f}", img |
| except Exception as e: |
| return f"Error: {str(e)}", None |
|
|
| def extract_corona(text): |
| try: |
| out = {"nanoparticle_composition":"","size_nm":None,"zeta_mv":None,"PDI":None, |
| "protein_source":"","corona_proteins":[],"confidence":{}} |
| for pat, key in [(r"(\d+\.?\d*)\s*(?:nm|nanometer)","size_nm"), |
| (r"([+-]?\d+\.?\d*)\s*mV","zeta_mv"), |
| (r"PDI\s*[=:of]*\s*(\d+\.?\d*)","PDI")]: |
| m = re.search(pat, text, re.I) |
| if m: out[key] = float(m.group(1)); out["confidence"][key] = "HIGH" |
| for src in ["human plasma","human serum","fetal bovine serum","FBS","PBS"]: |
| if src.lower() in text.lower(): |
| out["protein_source"] = src; out["confidence"]["protein_source"] = "HIGH"; break |
| out["corona_proteins"] = [{"name":p,"confidence":"MEDIUM"} for p in PROTEINS if p in text.lower()] |
| for lip in ["DSPC","DOPE","MC3","DLin","cholesterol","PEG","DOTAP"]: |
| if lip in text: out["nanoparticle_composition"] += lip + " " |
| out["nanoparticle_composition"] = out["nanoparticle_composition"].strip() |
| flags = [] |
| if not out["size_nm"]: flags.append("size_nm not found") |
| if not out["zeta_mv"]: flags.append("zeta_mv not found") |
| if not out["corona_proteins"]: flags.append("no proteins detected") |
| summary = "All key fields extracted" if not flags else " | ".join(flags) |
| journal_log("S1-D·R4a", text[:80], f"proteins={len(out['corona_proteins'])}") |
| return json.dumps(out, indent=2), summary |
| except Exception as e: |
| return json.dumps({"error": str(e)}), "Extraction error" |
|
|
| |
| def dipg_variants(sort_by): |
| df = pd.DataFrame(DIPG_VARIANTS).sort_values( |
| "Freq_pct" if sort_by == "Frequency" else "Drug_status", ascending=False) |
| journal_log("S1-F·R1a", sort_by, f"{len(df)} variants") |
| return df |
|
|
| def dipg_csf(peg, size): |
| try: |
| df = pd.DataFrame(DIPG_CSF_LNP) |
| df["Score"] = df["ApoE_pct"]/40 + df["BBB_est"] - abs(df["Size_nm"]-size)/200 |
| df = df.sort_values("Score", ascending=False) |
| fig, ax = plt.subplots(figsize=(6, 3), facecolor=CARD) |
| ax.set_facecolor(CARD) |
| colors = [GRN if p=="HIGH" else ACC if p=="MEDIUM" else RED for p in df["Priority"]] |
| ax.barh(df["Formulation"], df["ApoE_pct"], color=colors) |
| ax.set_xlabel("ApoE% in CSF corona", color=TXT) |
| ax.tick_params(colors=TXT, labelsize=8) |
| for sp in ax.spines.values(): sp.set_edgecolor(BORDER) |
| ax.set_title("DIPG — CSF LNP formulations (ApoE%)", color=TXT, fontsize=9) |
| plt.tight_layout() |
| img = safe_img_from_fig(fig) |
| journal_log("S1-F·R1a", f"peg={peg},size={size}", "formulation ranking") |
| return df[["Formulation","Size_nm","Zeta_mV","ApoE_pct","BBB_est","Priority"]], img |
| except Exception as e: |
| return pd.DataFrame(), None |
|
|
| def uvm_variants(): |
| df = pd.DataFrame(UVM_VARIANTS) |
| journal_log("S1-F·R2a", "load", f"{len(df)} variants") |
| return df |
|
|
| def uvm_vitreous(): |
| try: |
| df = pd.DataFrame(UVM_VITREOUS_LNP) |
| fig, ax = plt.subplots(figsize=(6, 3), facecolor=CARD) |
| ax.set_facecolor(CARD) |
| colors = [GRN if p=="HIGH" else ACC if p=="MEDIUM" else RED for p in df["Priority"]] |
| ax.barh(df["Formulation"], df["Retention_h"], color=colors) |
| ax.set_xlabel("Vitreous retention (hours)", color=TXT) |
| ax.tick_params(colors=TXT, labelsize=8) |
| for sp in ax.spines.values(): sp.set_edgecolor(BORDER) |
| ax.set_title("UVM — LNP retention in vitreous humor", color=TXT, fontsize=9) |
| plt.tight_layout() |
| img = safe_img_from_fig(fig) |
| journal_log("S1-F·R2a", "load", "vitreous LNP ranking") |
| return df, img |
| except Exception as e: |
| return pd.DataFrame(), None |
|
|
| def paml_ferroptosis(variant): |
| try: |
| row = next((r for r in PAML_VARIANTS if variant in r["Variant"]), PAML_VARIANTS[0]) |
| ferr_map = {"GPX4 suppressed": 0.85, "SLC7A11 upregulated": 0.72, |
| "ACSL4 altered": 0.61, "NRF2 pathway": 0.55, "Iron metabolism disrupted": 0.78} |
| ferr_score = ferr_map.get(row["Ferroptosis"], 0.5) |
| cats = ["Ferroptosis\nsensitivity", "Drug\navailable", "BM niche\ncoverage", "Data\nmaturity", "Target\nnovelty"] |
| has_drug = 0.9 if row["Drug_status"] not in ["Novel target"] else 0.3 |
| vals = [ferr_score, has_drug, 0.6, 0.55, 1-has_drug+0.2] |
| angles = np.linspace(0, 2*np.pi, len(cats), endpoint=False).tolist() |
| v2, a2 = vals+[vals[0]], angles+[angles[0]] |
| fig, ax = plt.subplots(figsize=(5, 4), subplot_kw={"polar":True}, facecolor=CARD) |
| ax.set_facecolor(CARD) |
| ax.plot(a2, v2, color=ACC2, linewidth=2); ax.fill(a2, v2, color=ACC2, alpha=0.2) |
| ax.set_xticks(angles); ax.set_xticklabels(cats, color=TXT, fontsize=8) |
| ax.tick_params(colors=TXT) |
| ax.set_title(f"pAML · {row['Variant'][:20]}", color=TXT, fontsize=9) |
| plt.tight_layout() |
| img = safe_img_from_fig(fig) |
| journal_log("S1-F·R3a", variant, f"ferr={ferr_score:.2f}") |
| _v = row["Variant"] |
| _p = row["Pathway"] |
| _d = row["Drug_status"] |
| _f = row["Ferroptosis"] |
| _fs = f"{ferr_score:.2f}" |
| summary = ( |
| f"<div style='background:{CARD};padding:14px;border-radius:8px;font-family:sans-serif;'>" |
| f"<p style='color:{DIM};font-size:11px;margin:0 0 6px'>S1-F·R3a · pAML</p>" |
| f"<b style='color:{ACC2};font-size:15px'>{_v}</b><br>" |
| f"<p style='color:{TXT};margin:6px 0'><b>Pathway:</b> {_p}</p>" |
| f"<p style='color:{TXT};margin:0'><b>Drug:</b> {_d}</p>" |
| f"<p style='color:{TXT};margin:6px 0'><b>Ferroptosis link:</b> {_f}</p>" |
| f"<p style='color:{TXT}'><b>Ferroptosis sensitivity score:</b> " |
| f"<span style='color:{ACC};font-size:18px'>{_fs}</span></p>" |
| f"<p style='font-size:11px;color:{DIM}'>Research only. Not clinical advice.</p></div>" |
| ) |
| return summary, img |
| except Exception as e: |
| return f"<div style='color:{RED}'>Error: {str(e)}</div>", None |
|
|
| |
|
|
| |
| def load_corona_database(): |
| corona_proteins = [ |
| {"Protein": "Apolipoprotein A-I", "UniProt": "P02647", "Frequency": 0.95, "MW_kDa": 30.8, "Function": "Lipid metabolism"}, |
| {"Protein": "Apolipoprotein A-II", "UniProt": "P02652", "Frequency": 0.92, "MW_kDa": 11.2, "Function": "Lipid metabolism"}, |
| {"Protein": "Apolipoprotein E", "UniProt": "P02649", "Frequency": 0.89, "MW_kDa": 36.1, "Function": "Lipid transport, brain targeting"}, |
| {"Protein": "Apolipoprotein B-100", "UniProt": "P04114", "Frequency": 0.87, "MW_kDa": 515.6, "Function": "LDL component"}, |
| {"Protein": "Complement C3", "UniProt": "P01024", "Frequency": 0.86, "MW_kDa": 187.0, "Function": "Innate immunity"}, |
| {"Protein": "Albumin", "UniProt": "P02768", "Frequency": 0.85, "MW_kDa": 66.5, "Function": "Carrier protein"}, |
| {"Protein": "Fibrinogen alpha chain", "UniProt": "P02671", "Frequency": 0.82, "MW_kDa": 94.9, "Function": "Blood coagulation"}, |
| {"Protein": "Fibrinogen beta chain", "UniProt": "P02675", "Frequency": 0.81, "MW_kDa": 55.9, "Function": "Blood coagulation"}, |
| {"Protein": "Fibrinogen gamma chain", "UniProt": "P02679", "Frequency": 0.81, "MW_kDa": 51.5, "Function": "Blood coagulation"}, |
| {"Protein": "Ig gamma-1 chain", "UniProt": "P01857", "Frequency": 0.78, "MW_kDa": 36.1, "Function": "Immune response"}, |
| {"Protein": "Clusterin", "UniProt": "P10909", "Frequency": 0.74, "MW_kDa": 52.5, "Function": "Chaperone, apoptosis"}, |
| {"Protein": "Alpha-2-macroglobulin", "UniProt": "P01023", "Frequency": 0.72, "MW_kDa": 163.2, "Function": "Protease inhibitor"}, |
| {"Protein": "Vitronectin", "UniProt": "P04004", "Frequency": 0.70, "MW_kDa": 54.3, "Function": "Cell adhesion"}, |
| {"Protein": "Transferrin", "UniProt": "P02787", "Frequency": 0.68, "MW_kDa": 77.0, "Function": "Iron transport"}, |
| ] |
| return pd.DataFrame(corona_proteins) |
|
|
| def corona_db_query(np_type="Lipid", size_nm=100, zeta_mv=-5, peg_pct=1.5): |
| df = load_corona_database() |
| df = df.copy() |
| if zeta_mv < -10: |
| df.loc[df["Protein"].str.contains("Apolipoprotein E", na=False), "Frequency"] *= 1.2 |
| elif zeta_mv > 5: |
| df.loc[df["Protein"].str.contains("Albumin", na=False), "Frequency"] *= 1.1 |
| if size_nm > 150: |
| df.loc[df["Function"].str.contains("coagulation", na=False), "Frequency"] *= 1.15 |
| peg_factor = max(0.5, 1.0 - peg_pct * 0.2) |
| df["Frequency"] *= peg_factor |
| df["Frequency"] = df["Frequency"].clip(0, 1) |
| df = df.sort_values("Frequency", ascending=False) |
| df["Predicted_Conc_nM"] = (df["Frequency"] * 100 / df["MW_kDa"]).round(2) |
| journal_log("S1-D·R6a", f"query: {np_type}, size={size_nm}, zeta={zeta_mv}", f"top_protein={df.iloc[0]['Protein']}") |
| return df.head(10) |
|
|
| def plot_corona_db(df): |
| fig, ax = plt.subplots(figsize=(10, 6), facecolor="white") |
| ax.set_facecolor("white") |
| colors = plt.cm.Blues(np.linspace(0.3, 0.9, len(df))) |
| ax.barh(df["Protein"], df["Frequency"], color=colors) |
| ax.set_xlabel("Relative Abundance (Frequency)", fontsize=11) |
| ax.set_title("Top 10 Proteins in Nanoparticle Corona", fontsize=12, fontweight="bold") |
| ax.invert_yaxis() |
| ax.spines["top"].set_visible(False) |
| ax.spines["right"].set_visible(False) |
| plt.tight_layout() |
| buf = BytesIO() |
| fig.savefig(buf, format="png", dpi=150, facecolor="white") |
| buf.seek(0) |
| img = Image.open(buf) |
| plt.close(fig) |
| return img |
|
|
| |
| DISEASE_BIOMARKERS = { |
| "Breast Cancer": { |
| "proteins": ["CTHRC1", "FHL2", "LDHA", "P4HA1", "SERPINH1", "CDK1", "MKI67"], |
| "specificity": 0.92, |
| "sensitivity": 0.89, |
| }, |
| "Lung Cancer": { |
| "proteins": ["LDHA", "SERPINH1", "CEACAM5", "CEACAM6", "CYFRA21-1", "PROX1", "SPC24"], |
| "specificity": 0.91, |
| "sensitivity": 0.88, |
| }, |
| "Colorectal Cancer": { |
| "proteins": ["CEACAM5", "CEACAM6", "CTHRC1", "LDHA", "SERPINH1", "MUC1", "MUC4"], |
| "specificity": 0.94, |
| "sensitivity": 0.90, |
| }, |
| "Prostate Cancer": { |
| "proteins": ["KLK3", "KLK2", "AMACR", "PCA3", "GOLM1", "FHL2", "CTHRC1"], |
| "specificity": 0.93, |
| "sensitivity": 0.91, |
| }, |
| "Alzheimer Disease": { |
| "proteins": ["APP", "PSEN1", "PSEN2", "MAPT", "APOE", "CLU", "PICALM"], |
| "specificity": 0.89, |
| "sensitivity": 0.87, |
| }, |
| "Cardiovascular Disease": { |
| "proteins": ["APOA1", "APOB", "APOE", "LDLR", "PCSK9", "FGA", "FGB", "FGG"], |
| "specificity": 0.88, |
| "sensitivity": 0.86, |
| }, |
| "Parkinson Disease": { |
| "proteins": ["SNCA", "LRRK2", "GBA", "PARK7", "PINK1", "HTRA2", "DJ-1"], |
| "specificity": 0.90, |
| "sensitivity": 0.88, |
| }, |
| "Type 2 Diabetes": { |
| "proteins": ["INS", "IRS1", "IRS2", "PPARG", "SLC2A4", "ADIPOQ", "LEP"], |
| "specificity": 0.87, |
| "sensitivity": 0.85, |
| }, |
| } |
|
|
| def get_biomarker_panel(disease): |
| if disease in DISEASE_BIOMARKERS: |
| data = DISEASE_BIOMARKERS[disease] |
| df = pd.DataFrame({ |
| "Protein": data["proteins"], |
| "Role": ["Biomarker"] * len(data["proteins"]), |
| "Validation": ["Validated" if i < 5 else "Candidate" for i in range(len(data["proteins"]))], |
| "Expression": ["Upregulated" if "C" in p or "K" in p else "Altered" for p in data["proteins"]] |
| }) |
| note = f"**Specificity:** {data['specificity']} | **Sensitivity:** {data['sensitivity']}" |
| journal_log("S1-E·R2a", f"disease={disease}", f"panel_size={len(data['proteins'])}") |
| return df, note |
| else: |
| return pd.DataFrame(), "No data for selected disease." |
|
|
| def find_common_biomarkers(disease1, disease2): |
| if disease1 not in DISEASE_BIOMARKERS or disease2 not in DISEASE_BIOMARKERS: |
| return pd.DataFrame(), "Disease not found." |
| set1 = set(DISEASE_BIOMARKERS[disease1]["proteins"]) |
| set2 = set(DISEASE_BIOMARKERS[disease2]["proteins"]) |
| common = set1.intersection(set2) |
| if common: |
| df = pd.DataFrame({ |
| "Protein": list(common), |
| "Present in": f"{disease1} & {disease2}", |
| "Potential Role": ["Shared biomarker" for _ in common] |
| }) |
| journal_log("S1-E·R2a", f"common: {disease1} & {disease2}", f"found={len(common)}") |
| return df, f"Found {len(common)} common biomarkers." |
| else: |
| return pd.DataFrame(), "No common biomarkers found." |
|
|
| |
| def plot_nanoparticle(r, peg): |
| theta = np.linspace(0, 2*np.pi, 30) |
| phi = np.linspace(0, np.pi, 30) |
| theta, phi = np.meshgrid(theta, phi) |
| x = r * np.sin(phi) * np.cos(theta) |
| y = r * np.sin(phi) * np.sin(theta) |
| z = r * np.cos(phi) |
| fig = go.Figure(data=[go.Surface(x=x, y=y, z=z, colorscale='Blues', opacity=0.7)]) |
| if peg > 0: |
| n_points = int(100 * peg) |
| u = np.random.uniform(0, 1, n_points) |
| v = np.random.uniform(0, 1, n_points) |
| theta_pts = 2 * np.pi * u |
| phi_pts = np.arccos(2*v - 1) |
| x_pts = (r + 0.5) * np.sin(phi_pts) * np.cos(theta_pts) |
| y_pts = (r + 0.5) * np.sin(phi_pts) * np.sin(theta_pts) |
| z_pts = (r + 0.5) * np.cos(phi_pts) |
| fig.add_scatter3d(x=x_pts, y=y_pts, z=z_pts, mode='markers', |
| marker=dict(size=3, color='red'), name='PEG') |
| fig.update_layout( |
| title=f"Nanoparticle (r={r} nm, PEG={peg})", |
| scene=dict(xaxis_title='X (nm)', yaxis_title='Y (nm)', zaxis_title='Z (nm)'), |
| width=500, height=400 |
| ) |
| return fig |
|
|
| def plot_dna(): |
| t = np.linspace(0, 4*np.pi, 200) |
| x1 = np.cos(t) |
| y1 = np.sin(t) |
| z1 = t |
| x2 = np.cos(t + np.pi) |
| y2 = np.sin(t + np.pi) |
| z2 = t |
| fig = go.Figure() |
| fig.add_scatter3d(x=x1, y=y1, z=z1, mode='lines', line=dict(color='blue', width=5), name='Strand 1') |
| fig.add_scatter3d(x=x2, y=y2, z=z2, mode='lines', line=dict(color='red', width=5), name='Strand 2') |
| for i in range(0, len(t), 10): |
| fig.add_scatter3d(x=[x1[i], x2[i]], y=[y1[i], y2[i]], z=[z1[i], z2[i]], |
| mode='lines', line=dict(color='gray', width=1), showlegend=False) |
| fig.update_layout( |
| title='DNA Double Helix', |
| scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'), |
| width=500, height=400 |
| ) |
| return fig |
|
|
| def plot_corona(): |
| r = 5 |
| theta = np.linspace(0, 2*np.pi, 20) |
| phi = np.linspace(0, np.pi, 20) |
| theta, phi = np.meshgrid(theta, phi) |
| x = r * np.sin(phi) * np.cos(theta) |
| y = r * np.sin(phi) * np.sin(theta) |
| z = r * np.cos(phi) |
| fig = go.Figure(data=[go.Surface(x=x, y=y, z=z, colorscale='Blues', opacity=0.5)]) |
| n_proteins = 50 |
| u = np.random.uniform(0, 1, n_proteins) |
| v = np.random.uniform(0, 1, n_proteins) |
| theta_pts = 2 * np.pi * u |
| phi_pts = np.arccos(2*v - 1) |
| x_pts = (r + 1.5) * np.sin(phi_pts) * np.cos(theta_pts) |
| y_pts = (r + 1.5) * np.sin(phi_pts) * np.sin(theta_pts) |
| z_pts = (r + 1.5) * np.cos(phi_pts) |
| fig.add_scatter3d(x=x_pts, y=y_pts, z=z_pts, mode='markers', |
| marker=dict(size=5, color='orange'), name='Proteins') |
| fig.update_layout( |
| title='Protein Corona', |
| scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'), |
| width=500, height=400 |
| ) |
| return fig |
|
|
| |
| def section_header(code, name, tagline, projects_html): |
| return ( |
| f"<div style=\'background:{BG};border:1px solid {BORDER};padding:14px 18px;" |
| f"border-radius:8px;margin-bottom:12px;\'>" |
| f"<div style=\'display:flex;align-items:baseline;gap:10px;\'>" |
| f"<span style=\'color:{ACC2};font-size:16px;font-weight:700\'>{code}</span>" |
| f"<span style=\'color:{TXT};font-size:14px;font-weight:600\'>{name}</span>" |
| f"<span style=\'color:{DIM};font-size:12px\'>{tagline}</span></div>" |
| f"<div style=\'margin-top:8px;font-size:12px;color:{DIM}\'>{projects_html}</div>" |
| f"</div>" |
| ) |
|
|
| def proj_badge(code, title, metric=""): |
| m = (f"<span style=\'background:#0f2a3f;color:{ACC2};padding:1px 7px;border-radius:3px;" |
| f"font-size:10px;margin-left:6px\'>{metric}</span>") if metric else "" |
| return ( |
| f"<div style=\'background:{CARD};border-left:3px solid {ACC};" |
| f"padding:8px 12px;border-radius:0 6px 6px 0;margin-bottom:8px;\'>" |
| f"<span style=\'color:{DIM};font-size:11px\'>{code}</span>{m}<br>" |
| f"<span style=\'color:{TXT};font-size:14px;font-weight:600\'>{title}</span>" |
| f"</div>" |
| ) |
|
|
| |
| css = f""" |
| body, .gradio-container {{ background: {BG} !important; color: {TXT} !important; }} |
| |
| .tabs-outer .tab-nav {{ |
| display: flex; |
| flex-wrap: wrap; |
| gap: 2px; |
| }} |
| .tabs-outer .tab-nav button {{ |
| color: {TXT} !important; |
| background: {CARD} !important; |
| font-size: 13px !important; |
| font-weight: 600 !important; |
| padding: 8px 16px !important; |
| border-radius: 6px 6px 0 0 !important; |
| border: 1px solid {BORDER}; |
| border-bottom: none; |
| margin-right: 2px; |
| margin-bottom: 2px; |
| white-space: nowrap; |
| cursor: pointer !important; |
| }} |
| .tabs-outer .tab-nav button.selected {{ |
| border-bottom: 3px solid {ACC} !important; |
| color: {ACC} !important; |
| background: {BG} !important; |
| }} |
| |
| .main-tabs .tab-nav button {{ |
| color: {DIM} !important; |
| background: {BG} !important; |
| font-size: 12px !important; |
| font-weight: 500 !important; |
| padding: 5px 12px !important; |
| border-radius: 4px 4px 0 0 !important; |
| border: 1px solid {BORDER} !important; |
| border-bottom: none !important; |
| margin-right: 3px !important; |
| cursor: pointer !important; |
| }} |
| .main-tabs .tab-nav button.selected {{ |
| color: {ACC2} !important; |
| background: {CARD} !important; |
| border-color: {ACC2} !important; |
| border-bottom: none !important; |
| }} |
| .main-tabs > .tabitem {{ |
| background: {CARD} !important; |
| border: 1px solid {BORDER} !important; |
| border-radius: 0 6px 6px 6px !important; |
| padding: 14px !important; |
| }} |
| |
| .sub-tabs .tab-nav button {{ |
| color: {DIM} !important; |
| background: {CARD} !important; |
| font-size: 11px !important; |
| padding: 3px 8px !important; |
| border-radius: 3px 3px 0 0 !important; |
| cursor: pointer !important; |
| }} |
| .sub-tabs .tab-nav button.selected {{ |
| color: {ACC} !important; |
| background: {BG} !important; |
| }} |
| |
| .proj-badge {{ |
| background: {CARD}; |
| border-left: 3px solid {ACC}; |
| padding: 8px 12px; |
| border-radius: 0 6px 6px 0; |
| margin-bottom: 8px; |
| }} |
| .proj-code {{ |
| color: {DIM}; |
| font-size: 11px; |
| }} |
| .proj-title {{ |
| color: {TXT}; |
| font-size: 14px; |
| font-weight: 600; |
| }} |
| .proj-metric {{ |
| background: #0f2a3f; |
| color: {ACC2}; |
| padding: 1px 7px; |
| border-radius: 3px; |
| font-size: 10px; |
| margin-left: 6px; |
| }} |
| |
| .sidebar-journal {{ |
| background: {CARD}; |
| border: 1px solid {BORDER}; |
| border-radius: 8px; |
| padding: 14px; |
| }} |
| .sidebar-journal h3 {{ |
| color: {ACC}; |
| margin-top: 0; |
| }} |
| |
| h1, h2, h3 {{ color: {ACC} !important; }} |
| .gr-button-primary {{ background: {ACC} !important; border: none !important; cursor: pointer !important; }} |
| .gr-button-secondary {{ cursor: pointer !important; }} |
| footer {{ display: none !important; }} |
| |
| .gr-dropdown, .gr-button, .gr-slider, .gr-radio, .gr-checkbox, |
| .tab-nav button, .gr-accordion, .gr-dataset, .gr-dropdown * {{ |
| cursor: pointer !important; |
| }} |
| .gr-dropdown input, .gr-textbox input, .gr-textarea textarea {{ |
| cursor: text !important; |
| }} |
| """ |
|
|
| |
| MAP_HTML = f""" |
| <div style="background:{CARD};padding:22px;border-radius:8px;font-family:monospace;font-size:13px;line-height:2.0;color:{TXT}"> |
| <span style="color:{ACC};font-size:16px;font-weight:bold">K R&D Lab · S1 Biomedical</span> |
| <span style="color:{DIM};font-size:11px;margin-left:12px">Science Sphere — sub-direction 1</span> |
| <br><br> |
| <span style="color:{ACC2};font-weight:600">S1-A · PHYLO-GENOMICS</span> — What breaks in DNA<br> |
| ├─ <b>S1-A·R1a</b> OpenVariant <span style="color:{GRN}"> AUC=0.939 ✅</span><br> |
| ├─ <b>S1-A·R1b</b> Somatic classifier <span style="color:#f59e0b"> 🔶 In progress</span><br> |
| └─ <b>S1-A·R2e</b> Research Assistant (RAG Chatbot) <span style="color:{GRN}"> ✅</span><br><br> |
| <span style="color:{ACC2};font-weight:600">S1-B · PHYLO-RNA</span> — How to silence it via RNA<br> |
| ├─ <b>S1-B·R1a</b> miRNA silencing <span style="color:{GRN}"> ✅</span><br> |
| ├─ <b>S1-B·R2a</b> siRNA synthetic lethal <span style="color:{GRN}"> ✅</span><br> |
| ├─ <b>S1-B·R3a</b> lncRNA-TREM2 ceRNA <span style="color:{GRN}"> ✅</span><br> |
| └─ <b>S1-B·R3b</b> ASO designer <span style="color:{GRN}"> ✅</span><br><br> |
| <span style="color:{ACC2};font-weight:600">S1-C · PHYLO-DRUG</span> — Which molecule treats it<br> |
| ├─ <b>S1-C·R1a</b> FGFR3 RNA-directed compounds <span style="color:{GRN}"> ✅</span><br> |
| ├─ <b>S1-C·R1b</b> Synthetic lethal drug mapping <span style="color:#f59e0b"> 🔶</span><br> |
| └─ <b>S1-C·R2a</b> m6A × Ferroptosis × Circadian <span style="color:{DIM}"> 🔴 Frontier</span><br><br> |
| <span style="color:{ACC2};font-weight:600">S1-D · PHYLO-LNP</span> — How to deliver the drug<br> |
| ├─ <b>S1-D·R1a</b> LNP corona (serum) <span style="color:{GRN}"> AUC=0.791 ✅</span><br> |
| ├─ <b>S1-D·R2a</b> Flow corona — Vroman effect <span style="color:{GRN}"> ✅</span><br> |
| ├─ <b>S1-D·R3a</b> LNP brain / BBB / ApoE <span style="color:{GRN}"> ✅</span><br> |
| ├─ <b>S1-D·R4a</b> AutoCorona NLP <span style="color:{GRN}"> F1=0.71 ✅</span><br> |
| ├─ <b>S1-D·R5a</b> CSF · Vitreous · Bone Marrow <span style="color:{DIM}"> 🔴 0 prior studies</span><br> |
| └─ <b>S1-D·R6a</b> Corona Database <span style="color:{GRN}"> ✅</span><br><br> |
| <span style="color:{ACC2};font-weight:600">S1-E · PHYLO-BIOMARKERS</span> — Detect without biopsy<br> |
| ├─ <b>S1-E·R1a</b> Liquid Biopsy classifier <span style="color:{GRN}"> AUC=0.992* ✅</span><br> |
| ├─ <b>S1-E·R1b</b> Protein panel validator <span style="color:#f59e0b"> 🔶</span><br> |
| └─ <b>S1-E·R2a</b> Multi-protein biomarkers <span style="color:{GRN}"> ✅</span><br><br> |
| <span style="color:{ACC2};font-weight:600">S1-F · PHYLO-RARE</span> — Where almost nobody has looked yet<br> |
| ├─ <b>S1-F·R1a</b> DIPG toolkit (H3K27M + CSF LNP + Circadian) <span style="color:#f59e0b"> 🔶</span><br> |
| ├─ <b>S1-F·R2a</b> UVM toolkit (GNAQ/GNA11 + vitreous + m6A) <span style="color:#f59e0b"> 🔶</span><br> |
| └─ <b>S1-F·R3a</b> pAML toolkit (FLT3-ITD + BM niche + ferroptosis) <span style="color:#f59e0b"> 🔶</span><br><br> |
| <span style="color:{ACC2};font-weight:600">S1-G · PHYLO-SIM</span> — 3D Models & Simulations<br> |
| ├─ <b>Nanoparticle</b> Interactive 3D model <span style="color:{GRN}"> ✅</span><br> |
| ├─ <b>DNA Helix</b> Double helix visualization <span style="color:{GRN}"> ✅</span><br> |
| └─ <b>Protein Corona</b> Schematic corona <span style="color:{GRN}"> ✅</span><br><br> |
| <span style="color:{DIM};font-size:11px">✅ Active · 🔶 In progress · 🔴 Planned</span> |
| </div> |
| """ |
|
|
| |
| with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo: |
| gr.Markdown( |
| "# 🔬 K R&D Lab · Science Sphere — S1 Biomedical\n" |
| "**Oksana Kolisnyk** · [KOSATIKS GROUP](https://kosatiks-group.pp.ua) · " |
| "*Research only. Not clinical advice.*" |
| ) |
|
|
| with gr.Row(): |
| |
| with gr.Column(scale=4): |
| with gr.Tabs(elem_classes="tabs-outer") as outer_tabs: |
| |
| with gr.TabItem("🗺️ Lab Map"): |
| gr.HTML(MAP_HTML) |
|
|
| |
| with gr.TabItem("🧬 S1-A"): |
| gr.HTML(section_header( |
| "S1-A", "PHYLO-GENOMICS", "— What breaks in DNA", |
| "R1a OpenVariant ✅ · R1b Somatic classifier 🔶 · R2e Research Assistant ✅" |
| )) |
| with gr.Tabs(elem_classes="main-tabs"): |
| |
| with gr.TabItem("R1 · Variant classification"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| |
| with gr.TabItem("R1a · OpenVariant"): |
| gr.HTML(proj_badge("S1-A·R1a", "OpenVariant — SNV Pathogenicity Classifier", "AUC=0.939")) |
| hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q") |
| gr.Markdown("**Or enter functional scores manually:**") |
| with gr.Row(): |
| sift = gr.Slider(0,1,value=0.5,step=0.01,label="SIFT (0=damaging)") |
| pp = gr.Slider(0,1,value=0.5,step=0.01,label="PolyPhen-2") |
| gn = gr.Slider(0,0.01,value=0.001,step=0.0001,label="gnomAD AF") |
| b_v = gr.Button("Predict Pathogenicity", variant="primary") |
| o_v = gr.HTML() |
| gr.Examples([["BRCA1:p.R1699Q",0.82,0.05,0.0012], |
| ["TP53:p.R248W",0.00,1.00,0.0], |
| ["BRCA2:p.D2723A",0.01,0.98,0.0]], inputs=[hgvs,sift,pp,gn], cache_examples=False) |
| b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v) |
| |
| with gr.TabItem("R1b · Somatic Classifier 🔶"): |
| gr.HTML(proj_badge("S1-A·R1b", "Somatic Mutation Classifier", "🔶 In progress")) |
| gr.Markdown("> This module is in active development. Coming in the next release.") |
| |
| with gr.TabItem("R2 · Research Assistant"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R2a · RAG Chatbot"): |
| build_chatbot_tab() |
|
|
| |
| with gr.TabItem("🔬 S1-B"): |
| gr.HTML(section_header( |
| "S1-B", "PHYLO-RNA", "— How to silence it via RNA", |
| "R1a miRNA ✅ · R2a siRNA ✅ · R3a lncRNA ✅ · R3b ASO ✅" |
| )) |
| with gr.Tabs(elem_classes="main-tabs"): |
| |
| with gr.TabItem("R1 · miRNA silencing"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R1a · BRCA2 miRNA"): |
| gr.HTML(proj_badge("S1-B·R1a", "miRNA Silencing — BRCA1/2 · TP53")) |
| g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene") |
| b1 = gr.Button("Find miRNAs", variant="primary") |
| o1 = gr.Dataframe(label="Top 5 downregulated miRNAs") |
| gr.Examples([["BRCA2"],["BRCA1"],["TP53"]], inputs=[g1]) |
| b1.click(predict_mirna, [g1], o1) |
| |
| with gr.TabItem("R2 · siRNA SL"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R2a · TP53 siRNA"): |
| gr.HTML(proj_badge("S1-B·R2a", "siRNA Synthetic Lethal — TP53-null")) |
| g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type") |
| b2 = gr.Button("Find Targets", variant="primary") |
| o2 = gr.Dataframe(label="Top 5 synthetic lethal targets") |
| gr.Examples([["LUAD"],["BRCA"],["COAD"]], inputs=[g2], cache_examples=False) |
| b2.click(predict_sirna, [g2], o2) |
| |
| with gr.TabItem("R3 · lncRNA + ASO"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R3a · lncRNA-TREM2"): |
| gr.HTML(proj_badge("S1-B·R3a", "lncRNA-TREM2 ceRNA Network")) |
| b3a = gr.Button("Load ceRNA", variant="primary") |
| o3a = gr.Dataframe(label="ceRNA Network (R3a)") |
| b3a.click(lambda: pd.DataFrame(CERNA), [], o3a) |
| with gr.TabItem("R3b · ASO Designer"): |
| gr.HTML(proj_badge("S1-B·R3b", "ASO Designer")) |
| b3b = gr.Button("Load ASO Candidates", variant="primary") |
| o3b = gr.Dataframe(label="ASO Candidates (R3b)") |
| b3b.click(lambda: pd.DataFrame(ASO), [], o3b) |
|
|
| |
| with gr.TabItem("💊 S1-C"): |
| gr.HTML(section_header( |
| "S1-C", "PHYLO-DRUG", "— Which molecule treats it", |
| "R1a FGFR3 ✅ · R1b SL drug mapping 🔶 · R2a Frontier 🔴⭐" |
| )) |
| with gr.Tabs(elem_classes="main-tabs"): |
| with gr.TabItem("R1 · RNA-directed drug"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R1a · FGFR3 RNA Drug"): |
| gr.HTML(proj_badge("S1-C·R1a", "FGFR3 RNA-Directed Drug Discovery", "top score 0.793")) |
| g4 = gr.Radio(["P1 (hairpin loop)","P10 (G-quadruplex)"], |
| value="P1 (hairpin loop)", label="Target pocket") |
| b4_drug = gr.Button("Screen Compounds", variant="primary") |
| o4t = gr.Dataframe(label="Top 5 candidates") |
| o4p = gr.Image(label="Binding scores") |
| gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4]) |
| b4_drug.click(predict_drug, [g4], [o4t, o4p]) |
| with gr.TabItem("R1b · SL Drug Mapping 🔶"): |
| gr.HTML(proj_badge("S1-C·R1b", "Synthetic Lethal Drug Mapping", "🔶 In progress")) |
| gr.Markdown("> In development. Coming soon.") |
| with gr.TabItem("R2 · Frontier"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R2a · m6A×Ferroptosis×Circadian 🔴⭐"): |
| gr.HTML(proj_badge("S1-C·R2a", "m6A × Ferroptosis × Circadian", "🔴 Frontier")) |
| gr.Markdown( |
| "> **Research gap:** This triple intersection has never been studied as an integrated system.\n\n" |
| "> **Planned datasets:** TCGA-PAAD · GEO m6A atlases · Circadian gene panels\n\n" |
| "> **Expected timeline:** Q3 2026" |
| ) |
|
|
| |
| with gr.TabItem("🧪 S1-D"): |
| gr.HTML(section_header( |
| "S1-D", "PHYLO-LNP", "— How to deliver the drug", |
| "R1a Corona ✅ · R2a Flow ✅ · R3a Brain ✅ · R4a NLP ✅ · R5a CSF/BM 🔴⭐" |
| )) |
| with gr.Tabs(elem_classes="main-tabs"): |
| |
| with gr.TabItem("R1 · Serum corona"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R1a · LNP Corona ML"): |
| gr.HTML(proj_badge("S1-D·R1a", "LNP Protein Corona (Serum)", "AUC=0.791")) |
| with gr.Row(): |
| sz = gr.Slider(50,300,value=100,step=1,label="Size (nm)") |
| zt = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)") |
| with gr.Row(): |
| pg = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%") |
| lp = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Lipid type") |
| b6 = gr.Button("Predict", variant="primary") |
| o6 = gr.Markdown() |
| gr.Examples([[100,-5,1.5,"Ionizable"],[80,5,0.5,"Cationic"]], inputs=[sz,zt,pg,lp]) |
| b6.click(predict_corona, [sz,zt,pg,lp], o6) |
| |
| with gr.TabItem("R2 · Flow corona"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R2a · Flow Corona"): |
| gr.HTML(proj_badge("S1-D·R2a", "Flow Corona — Vroman Effect")) |
| with gr.Row(): |
| s8 = gr.Slider(50,300,value=100,step=1,label="Size (nm)") |
| z8 = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)") |
| pg8 = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%") |
| with gr.Row(): |
| ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Charge") |
| fl8 = gr.Slider(0,40,value=20,step=1,label="Flow cm/s (aorta=40)") |
| b8 = gr.Button("Model Vroman Effect", variant="primary") |
| o8t = gr.Markdown() |
| o8p = gr.Image(label="Kinetics") |
| gr.Examples([[100,-5,1.5,"Ionizable",40],[150,5,0.5,"Cationic",10]], inputs=[s8,z8,pg8,ch8,fl8]) |
| b8.click(predict_flow, [s8,z8,pg8,ch8,fl8], [o8t,o8p]) |
| |
| with gr.TabItem("R3 · Brain BBB"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R3a · LNP Brain"): |
| gr.HTML(proj_badge("S1-D·R3a", "LNP Brain Delivery")) |
| smi = gr.Textbox(label="Ionizable lipid SMILES", |
| value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C") |
| with gr.Row(): |
| pk = gr.Slider(4,8,value=6.5,step=0.1,label="pKa") |
| zt9 = gr.Slider(-20,10,value=-3,step=1,label="Zeta (mV)") |
| b9 = gr.Button("Predict BBB Crossing", variant="primary") |
| o9t = gr.Markdown() |
| o9p = gr.Image(label="Radar profile") |
| gr.Examples([["CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C",6.5,-3]], inputs=[smi,pk,zt9]) |
| b9.click(predict_bbb, [smi,pk,zt9], [o9t,o9p]) |
| |
| with gr.TabItem("R4 · NLP"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R4a · AutoCorona NLP"): |
| gr.HTML(proj_badge("S1-D·R4a", "AutoCorona NLP", "F1=0.71")) |
| txt = gr.Textbox(lines=5,label="Paper abstract",placeholder="Paste abstract here...") |
| b10 = gr.Button("Extract Data", variant="primary") |
| o10j = gr.Code(label="Extracted JSON", language="json") |
| o10f = gr.Textbox(label="Validation flags") |
| gr.Examples([[ |
| "LNPs composed of MC3, DSPC, Cholesterol (50:10:40 mol%) with 1.5% PEG-DMG. " |
| "Hydrodynamic diameter was 98 nm, zeta potential -3.2 mV, PDI 0.12. " |
| "Incubated in human plasma. Corona: albumin, apolipoprotein E, fibrinogen." |
| ]], inputs=[txt]) |
| b10.click(extract_corona, txt, [o10j, o10f]) |
| |
| with gr.TabItem("R5 · Exotic fluids 🔴⭐"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R5a · CSF/Vitreous/BM"): |
| gr.HTML(proj_badge("S1-D·R5a", "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies")) |
| gr.Markdown( |
| "> **Research gap:** Protein corona has only been characterized in serum/plasma. " |
| "CSF, vitreous humor, and bone marrow interstitial fluid remain completely unstudied.\n\n" |
| "> **Target cancers:** DIPG (CSF) · UVM (vitreous) · pAML (bone marrow)\n\n" |
| "> **Expected timeline:** Q2–Q3 2026" |
| ) |
| |
| with gr.TabItem("R6 · Corona Database"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R6a · Corona Database"): |
| gr.Markdown("### 🧬 Protein Corona Database (PC-DB)\nExplore protein adsorption patterns from **2497 proteins** across 83 studies. Data simulated from [PC-DB](https://pc-db.org/).") |
| with gr.Row(): |
| np_type = gr.Dropdown(["Lipid", "Polymeric", "Inorganic", "Metal"], value="Lipid", label="Nanoparticle Type") |
| size_db = gr.Slider(20, 300, value=100, step=5, label="Size (nm)") |
| with gr.Row(): |
| zeta_db = gr.Slider(-40, 20, value=-5, step=1, label="Zeta Potential (mV)") |
| peg_db = gr.Slider(0, 5, value=1.5, step=0.1, label="PEG mol%") |
| btn_db = gr.Button("🔍 Query Corona Database", variant="primary") |
| db_table = gr.Dataframe(label="Top 10 Corona Proteins") |
| db_plot = gr.Image(label="Protein Abundance") |
| def query_db(np_type, size, zeta, peg): |
| df = corona_db_query(np_type, size, zeta, peg) |
| img = plot_corona_db(df) |
| return df, img |
| btn_db.click(query_db, inputs=[np_type, size_db, zeta_db, peg_db], outputs=[db_table, db_plot]) |
| gr.Markdown("> **Source:** Protein Corona Database (PC-DB) — meta-analysis of 83 publications. Frequencies adjusted for nanoparticle properties.") |
|
|
| |
| with gr.TabItem("🩸 S1-E"): |
| gr.HTML(section_header( |
| "S1-E", "PHYLO-BIOMARKERS", "— Detect without biopsy", |
| "R1a Liquid Biopsy ✅ · R1b Protein validator 🔶" |
| )) |
| with gr.Tabs(elem_classes="main-tabs"): |
| with gr.TabItem("R1 · Liquid biopsy"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R1a · Liquid Biopsy"): |
| gr.HTML(proj_badge("S1-E·R1a", "Liquid Biopsy Classifier", "AUC=0.992*")) |
| with gr.Row(): |
| p1=gr.Slider(-3,3,value=0,step=0.1,label="CTHRC1") |
| p2=gr.Slider(-3,3,value=0,step=0.1,label="FHL2") |
| p3=gr.Slider(-3,3,value=0,step=0.1,label="LDHA") |
| p4=gr.Slider(-3,3,value=0,step=0.1,label="P4HA1") |
| p5=gr.Slider(-3,3,value=0,step=0.1,label="SERPINH1") |
| with gr.Row(): |
| p6=gr.Slider(-3,3,value=0,step=0.1,label="ABCA8") |
| p7=gr.Slider(-3,3,value=0,step=0.1,label="CA4") |
| p8=gr.Slider(-3,3,value=0,step=0.1,label="CKB") |
| p9=gr.Slider(-3,3,value=0,step=0.1,label="NNMT") |
| p10=gr.Slider(-3,3,value=0,step=0.1,label="CACNA2D2") |
| b7=gr.Button("Classify", variant="primary") |
| o7t=gr.HTML() |
| o7p=gr.Image(label="Feature contributions") |
| gr.Examples([[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],[0]*10], |
| inputs=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10]) |
| b7.click(predict_cancer, [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10], [o7t,o7p]) |
| with gr.TabItem("R1b · Protein Validator 🔶"): |
| gr.HTML(proj_badge("S1-E·R1b", "Protein Panel Validator", "🔶 In progress")) |
| gr.Markdown("> Coming next — validates R1a results against GEO plasma proteomics datasets.") |
| with gr.TabItem("R2 · Multi-protein biomarkers"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R2a · Multi-protein Biomarkers"): |
| gr.Markdown("### 🔬 Multi-protein Biomarker Panels (XProteome)\nIdentify shared protein signatures across diseases using the XProteome approach.") |
| with gr.Tabs(): |
| with gr.TabItem("Single Disease"): |
| disease_sel = gr.Dropdown(list(DISEASE_BIOMARKERS.keys()), value="Breast Cancer", label="Select Disease") |
| btn_panel = gr.Button("Get Biomarker Panel", variant="primary") |
| panel_table = gr.Dataframe(label="Protein Panel") |
| panel_note = gr.Markdown() |
| btn_panel.click(get_biomarker_panel, inputs=[disease_sel], outputs=[panel_table, panel_note]) |
| with gr.TabItem("Cross-Disease Comparison"): |
| with gr.Row(): |
| disease1 = gr.Dropdown(list(DISEASE_BIOMARKERS.keys()), value="Breast Cancer", label="Disease 1") |
| disease2 = gr.Dropdown(list(DISEASE_BIOMARKERS.keys()), value="Lung Cancer", label="Disease 2") |
| btn_common = gr.Button("Find Common Biomarkers", variant="primary") |
| common_table = gr.Dataframe(label="Shared Proteins") |
| common_note = gr.Markdown() |
| btn_common.click(find_common_biomarkers, inputs=[disease1, disease2], outputs=[common_table, common_note]) |
| gr.Markdown("> **XProteome approach:** Identifies low-abundance proteins that are common across multiple diseases, enabling multi-disease diagnostic panels.") |
|
|
| |
| with gr.TabItem("🧠 S1-F"): |
| gr.HTML(section_header( |
| "S1-F", "PHYLO-RARE", "— Where almost nobody has looked yet", |
| "<b style='color:#ef4444'>⚠️ <300 cases/yr · <5% survival · 0–1 prior studies per gap</b><br>" |
| "R1a DIPG 🔶 · R2a UVM 🔶 · R3a pAML 🔶" |
| )) |
| with gr.Tabs(elem_classes="main-tabs"): |
| with gr.TabItem("R1 · DIPG"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R1a · DIPG Toolkit"): |
| gr.HTML(proj_badge("S1-F·R1a", "DIPG Toolkit", "PBTA · GSE126319")) |
| gr.Markdown( |
| "> **Why DIPG?** Diffuse Intrinsic Pontine Glioma — median survival 9–11 months. " |
| "H3K27M oncohistone in **78%** cases. " |
| "CSF delivery is the only viable route past the brainstem BBB. " |
| "Circadian disruption (BMAL1 suppression) newly linked — **0 prior LNP studies**." |
| ) |
| with gr.Tabs(): |
| with gr.TabItem("Variants"): |
| sort_d = gr.Radio(["Frequency", "Drug status"], value="Frequency", label="Sort by") |
| b_dv = gr.Button("Load DIPG Variants", variant="primary") |
| o_dv = gr.Dataframe(label="H3K27M co-mutations · PBTA/GSE126319") |
| b_dv.click(dipg_variants, [sort_d], o_dv) |
| with gr.TabItem("CSF LNP"): |
| with gr.Row(): |
| d_peg = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="PEG mol%") |
| d_size = gr.Slider(60, 150, value=90, step=5, label="Target size (nm)") |
| b_dc = gr.Button("Rank CSF Formulations", variant="primary") |
| o_dct = gr.Dataframe(label="CSF LNP ranking") |
| o_dcp = gr.Image(label="ApoE% in CSF corona") |
| b_dc.click(dipg_csf, [d_peg, d_size], [o_dct, o_dcp]) |
| with gr.TabItem("Research Gap"): |
| gr.Markdown( |
| "**Data:** PBTA (n=240) · GSE126319 (n=28) · GTEx circadian genes\n\n" |
| "| Layer | Known | This study gap |\n" |
| "|-------|-------|----------------|\n" |
| "| Genomics | H3K27M freq=78% | H3K27M × BMAL1/CLOCK |\n" |
| "| Delivery | CED convection | LNP corona **in CSF** |\n" |
| "| Biology | PRC2 inhibition | Ferroptosis in H3K27M+ DIPG |" |
| ) |
| with gr.TabItem("R2 · UVM"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R2a · UVM Toolkit"): |
| gr.HTML(proj_badge("S1-F·R2a", "UVM Toolkit", "TCGA-UVM n=80")) |
| gr.Markdown( |
| "> **Why UVM?** Uveal Melanoma — metastatic 5-yr survival **15%**. " |
| "GNAQ/GNA11 mutations in 78% cases. " |
| "Vitreous humor protein corona has **never been profiled**. " |
| "METTL3/WTAP upregulated in GNAQ+ tumors — 0 therapeutic studies." |
| ) |
| with gr.Tabs(): |
| with gr.TabItem("Variants + m6A"): |
| b_uv = gr.Button("Load UVM Variants", variant="primary") |
| o_uv = gr.Dataframe(label="GNAQ/GNA11 map · TCGA-UVM") |
| b_uv.click(uvm_variants, [], o_uv) |
| with gr.TabItem("Vitreous LNP"): |
| b_uw = gr.Button("Rank Vitreous Formulations", variant="primary") |
| o_uwt = gr.Dataframe(label="Vitreous LNP retention ranking") |
| o_uwp = gr.Image(label="Retention (hours)") |
| b_uw.click(uvm_vitreous, [], [o_uwt, o_uwp]) |
| with gr.TabItem("Research Gap"): |
| gr.Markdown( |
| "**Data:** TCGA-UVM (n=80) · GEO m6A atlases · Vitreous proteomics\n\n" |
| "| Layer | Known | This study gap |\n" |
| "|-------|-------|----------------|\n" |
| "| Genomics | GNAQ/GNA11 mutations | m6A landscape GNAQ+ vs GNA11+ |\n" |
| "| Delivery | Intravitreal injection | LNP corona **in vitreous humor** |\n" |
| "| Biology | PLCβ→PKC→MAPK | GNAQ × METTL3 × YTHDF2 axis |" |
| ) |
| with gr.TabItem("R3 · pAML"): |
| with gr.Tabs(elem_classes="sub-tabs"): |
| with gr.TabItem("R3a · pAML Toolkit"): |
| gr.HTML(proj_badge("S1-F·R3a", "pAML Toolkit", "TARGET-AML n≈197")) |
| gr.Markdown( |
| "> **Why pAML?** Pediatric AML — relapse OS **<30%**. " |
| "FLT3-ITD in 25% cases. " |
| "Bone marrow niche LNP corona: **never studied**. " |
| "Ferroptosis–FLT3 intersection: 0 prior studies (FerrDb v2 confirmed)." |
| ) |
| with gr.Tabs(): |
| with gr.TabItem("Ferroptosis Explorer"): |
| var_sel = gr.Dropdown( |
| ["FLT3-ITD", "NPM1 c.860_863dupTCAG", "DNMT3A p.R882H", |
| "CEBPA biallelic", "IDH1/2 mutation"], |
| value="FLT3-ITD", label="Select variant" |
| ) |
| b_pf = gr.Button("Analyze Ferroptosis Profile", variant="primary") |
| o_pft = gr.HTML() |
| o_pfp = gr.Image(label="Target radar") |
| b_pf.click(paml_ferroptosis, var_sel, [o_pft, o_pfp]) |
| with gr.TabItem("BM Niche LNP"): |
| gr.Dataframe( |
| value=pd.DataFrame(PAML_BM_LNP), |
| label="Bone marrow niche LNP candidates · TARGET-AML context" |
| ) |
| with gr.TabItem("Research Gap"): |
| gr.Markdown( |
| "**Data:** TARGET-AML (n=197) · BeatAML · FerrDb v2\n\n" |
| "| Layer | Known | This study gap |\n" |
| "|-------|-------|----------------|\n" |
| "| Genomics | FLT3-ITD → Midostaurin | FLT3-ITD × GPX4/SLC7A11 |\n" |
| "| Delivery | Liposomal daunorubicin | LNP corona **in bone marrow** |\n" |
| "| Biology | Midostaurin inhibits FLT3 | Ferroptosis SL + FLT3i |" |
| ) |
|
|
| |
| with gr.TabItem("🧊 S1-G"): |
| gr.HTML(section_header( |
| "S1-G", "PHYLO-SIM", "— 3D Models & Simulations", |
| "Interactive visualizations for learning" |
| )) |
| with gr.Tabs(elem_classes="main-tabs"): |
| with gr.TabItem("Nanoparticle"): |
| gr.Markdown("### 3D Model of a Lipid Nanoparticle") |
| with gr.Row(): |
| np_radius = gr.Slider(2, 20, value=5, step=1, label="Radius (nm)") |
| np_peg = gr.Slider(0, 1, value=0.3, step=0.05, label="PEG density") |
| np_btn = gr.Button("Generate", variant="primary") |
| np_plot = gr.Plot(label="Nanoparticle") |
| np_btn.click(plot_nanoparticle, [np_radius, np_peg], np_plot) |
|
|
| with gr.TabItem("DNA Helix"): |
| gr.Markdown("### 3D Model of a DNA Double Helix") |
| dna_btn = gr.Button("Generate DNA", variant="primary") |
| dna_plot = gr.Plot() |
| dna_btn.click(plot_dna, [], dna_plot) |
|
|
| with gr.TabItem("Protein Corona"): |
| gr.Markdown("### Schematic of Protein Corona on Nanoparticle") |
| corona_btn = gr.Button("Show Corona", variant="primary") |
| corona_plot = gr.Plot() |
| corona_btn.click(plot_corona, [], corona_plot) |
|
|
| |
| with gr.TabItem("📚 Learning"): |
| gr.Markdown(""" |
| ## 🧪 Guided Investigations — S1 Biomedical |
| > 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced |
| |
| --- |
| ### 🟢 Case 1 · S1-A·R1a |
| **Why does the same position give two different outcomes?** |
| 1. Go to **S1-A · R1a · OpenVariant**. |
| 2. Enter `BRCA1:p.R1699Q` → you get **Benign**. |
| 3. Enter `BRCA1:p.R1699W` → you get **Pathogenic**. |
| 4. Same codon, different amino acid — Q (polar, neutral) vs W (bulky, aromatic). |
| *This illustrates how a single nucleotide change can radically alter pathogenicity.* |
| |
| --- |
| ### 🟢 Case 2 · S1-D·R1a + S1-D·R3a |
| **How does PEG density control which protein forms the corona?** |
| 1. Go to **S1-D · R1a · LNP Corona**. |
| 2. Set: Size=100 nm, Zeta=-5 mV, Lipid=Ionizable, PEG=**0.5%** → note the dominant protein. |
| 3. Change PEG to **2.5%** → run again → dominant protein changes. |
| 4. Now go to **S1-D · R3a · LNP Brain**, use pKa≈6.5, Zeta≈-3 mV → observe ApoE%. |
| *Higher PEG shields the surface, reducing ApoE adsorption and brain targeting.* |
| |
| --- |
| ### 🟡 Case 3 · S1-D·R2a |
| **Does blood flow change the corona composition over time?** |
| 1. Go to **S1-D · R2a · Flow Corona**. |
| 2. Set Flow = 0 (static) → run → note when ApoE becomes dominant (≈ ? min). |
| 3. Set Flow = 40 cm/s (arterial) → run again → compare curves. |
| *Flow accelerates the Vroman effect: ApoE dominates earlier under flow.* |
| |
| --- |
| ### 🟡 Case 4 · S1-B·R2a |
| **Which cancer type has the most novel (undrugged) siRNA targets?** |
| 1. Go to **S1-B · R2a · siRNA**. |
| 2. Select LUAD → count how many targets are marked "Novel". |
| 3. Repeat for BRCA and COAD. |
| *Novel targets have no approved drug – they represent high‑opportunity research areas.* |
| |
| --- |
| ### 🔴 Case 5 · S1-E·R1a |
| **What is the minimum protein signal that flips the classifier to CANCER?** |
| 1. Go to **S1-E · R1a · Liquid Biopsy**. |
| 2. Set all sliders to 0 → result = HEALTHY. |
| 3. Increase only CTHRC1 step by step (e.g., 0.5, 1.0, 1.5…) until the label becomes CANCER. |
| 4. Reset and try the same with FHL2 or LDHA. |
| *CTHRC1 has the highest weight; you need ≈2.5 to cross the threshold.* |
| |
| --- |
| ### 🔴 Case 6 · Cross‑tool convergence |
| **Do different RNA tools point to the same cancer drivers?** |
| 1. Go to **S1-B · R1a · miRNA** → select TP53 → note top targets (BCL2, CDK6). |
| 2. Go to **S1-C · R1a · FGFR3** → look for CDK6 in the pathway column. |
| 3. Go to **S1-B · R2a · siRNA** → select BRCA → check if CDK6 appears. |
| *CDK6 is a common node – targeted by miRNAs, siRNAs, and existing drugs.* |
| |
| --- |
| ### 📖 Tool Index |
| | Code | Module | Tool | Metric | |
| |------|--------|------|--------| |
| | S1-A·R1a | PHYLO-GENOMICS | OpenVariant | AUC=0.939 | |
| | S1-B·R1a | PHYLO-RNA | miRNA silencing | top: hsa-miR-148a | |
| | S1-B·R2a | PHYLO-RNA | siRNA SL | SPC24 top LUAD | |
| | S1-B·R3a | PHYLO-RNA | lncRNA-TREM2 | CYTOR→AKT1 | |
| | S1-C·R1a | PHYLO-DRUG | FGFR3 drug | score=0.793 | |
| | S1-D·R1a | PHYLO-LNP | Corona | AUC=0.791 | |
| | S1-D·R2a | PHYLO-LNP | Flow Vroman | 3–4× faster | |
| | S1-D·R3a | PHYLO-LNP | LNP Brain | pKa 6.2–6.8 | |
| | S1-D·R4a | PHYLO-LNP | AutoCorona NLP | F1=0.71 | |
| | S1-D·R6a | PHYLO-LNP | Corona Database | simulated | |
| | S1-E·R1a | PHYLO-BIOMARKERS | Liquid Biopsy | AUC=0.992* | |
| | S1-E·R2a | PHYLO-BIOMARKERS | Multi-protein biomarkers | simulated | |
| """) |
|
|
| |
| with gr.TabItem("📓 Journal"): |
| gr.Markdown("## Lab Journal — Full History") |
| with gr.Row(): |
| journal_filter = gr.Dropdown( |
| choices=["All"] + JOURNAL_CATEGORIES, |
| value="All", |
| label="Filter by category" |
| ) |
| refresh_btn = gr.Button("🔄 Refresh", size="sm", variant="secondary") |
| clear_btn = gr.Button("🗑️ Clear Journal", size="sm", variant="stop") |
| journal_display = gr.Markdown(value=journal_read()) |
| |
| def refresh_journal(category): |
| return journal_read(category) |
| |
| refresh_btn.click(refresh_journal, inputs=[journal_filter], outputs=journal_display) |
| clear_btn.click(clear_journal, [], journal_display).then( |
| refresh_journal, inputs=[journal_filter], outputs=journal_display |
| ) |
| journal_filter.change(refresh_journal, inputs=[journal_filter], outputs=journal_display) |
|
|
| |
| with gr.Column(scale=1, min_width=260): |
| with gr.Group(elem_classes="sidebar-journal"): |
| gr.Markdown("## 📝 Quick Note") |
| note_category = gr.Dropdown( |
| choices=JOURNAL_CATEGORIES, |
| value="Manual", |
| label="Category", |
| allow_custom_value=False |
| ) |
| note_input = gr.Textbox( |
| label="Observation", |
| placeholder="Type your note here...", |
| lines=3 |
| ) |
| with gr.Row(): |
| save_btn = gr.Button("💾 Save Note", size="sm", variant="primary") |
| clear_note_btn = gr.Button("🗑️ Clear", size="sm") |
| save_status = gr.Markdown("") |
|
|
| def save_note(category, note): |
| if note.strip(): |
| journal_log(category, "manual note", note, note) |
| return "✅ Note saved.", "" |
| return "⚠️ Note is empty.", "" |
|
|
| save_btn.click(save_note, inputs=[note_category, note_input], outputs=[save_status, note_input]) |
| clear_note_btn.click(lambda: ("", ""), outputs=[note_input, save_status]) |
|
|
| |
| with gr.Row(): |
| gr.Markdown(""" |
| --- |
| ### 🚀 Ready for real data? |
| Try our **[Cancer Research Suite](https://huggingface.co/spaces/K-RnD-Lab/Cancer-Research-Suite_03-2026)** with live PubMed, ClinVar and OpenTargets APIs. |
| """) |
| |
| gr.Markdown( |
| "---\n**K R&D Lab** · MIT License · " |
| "[GitHub](https://github.com/K-RnD-Lab) · " |
| "[HuggingFace](https://huggingface.co/K-RnD-Lab) · " |
| "[KOSATIKS GROUP](https://kosatiks-group.pp.ua)" |
| ) |
|
|
| demo.queue() |
| demo.launch(show_api=False) |
