sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
88cad557771990ed9804a3fc68401742d6735460a51d067acdcb2e5d814684b9 | Jupyter | 5,615 | 45 | # %% [markdown]
# # organagechronologicalheart
#
# Heart OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assa... |
19fb7de4e0cf47d6f7491b607ff2d66ef08084c6df00c56dcf1d3152dcbb023b | Jupyter | 5,616 | 45 | # %% [markdown]
# # organagechronologicalliver
#
# Liver OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assa... |
9e39ff0868f6f1755a50b12b6c8c8362ac730d525abc27daff511fbf0a0e0de9 | Jupyter | 5,617 | 45 | # %% [markdown]
# # organagechronologicalbrain
#
# Brain OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assa... |
b176f35f2905def0278a5c7f5470de097ea24c7b540f9a3a63bc738b945d55ad | Jupyter | 5,620 | 45 | # %% [markdown]
# # organagechronologicalkidney
#
# Kidney OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink as... |
4862d75ab89aa68784229dfb56c0f695ce4cccdbaa840a1ca2b0cab57181f265 | Jupyter | 5,621 | 45 | # %% [markdown]
# # organagechronologicalmuscle
#
# Muscle OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink as... |
a9e26883b80a90f5408431b49cbe8c8aec72603b684f823f67d5561773682432 | Jupyter | 5,621 | 45 | # %% [markdown]
# # organagechronologicalartery
#
# Artery OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink as... |
d864d47c9447a8ab23ed47e69e775a6551afdcd6929f0a19d877567c5afbbf51 | Jupyter | 5,622 | 45 | # %% [markdown]
# # organagechronologicalimmune
#
# Immune OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink as... |
0dae2017acdcbb03a104e8cd423a3faf6084cda7fc1b71b04c8dc69400511cc2 | Jupyter | 5,625 | 45 | # %% [markdown]
# # organagechronologicaladipose
#
# Adipose OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink ... |
477df8d37b2447b7eff5dbbadfb8eb87854f9cedb86d52f98e7f844fe3bf91d3 | Jupyter | 5,625 | 45 | # %% [markdown]
# # organagechronologicalthyroid
#
# Thyroid OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink ... |
d52b5961cd983db7ac34b25db4f96ad0359892ed27292b9ef1d0e1f684a8126f | Jupyter | 5,625 | 45 | # %% [markdown]
# # organagechronologicaladrenal
#
# Adrenal OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink ... |
60a570ecdda8c41b7dde0710f7e29077d411e4416b00bb0c083430ee6ac2ccd5 | Jupyter | 5,626 | 45 | # %% [markdown]
# # organagechronologicalstomach
#
# Stomach OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink ... |
5905ddbb784bae9695f1487fca54a45f53e32526feb0d523fae24169f8bf724a | Jupyter | 5,630 | 182 | # %% [markdown]
# # epiTOC2
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-in... |
009d62da55a96ba96416be683ad7db5174f465a2aa1810917103b4e339f8d000 | Jupyter | 5,631 | 45 | # %% [markdown]
# # organagechronologicalsalivary
#
# Salivary OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olin... |
3dbcce4adcf9b87ef0c8408e1ffaae52ce977036785d7e07a520e81b0c00961c | Jupyter | 5,631 | 45 | # %% [markdown]
# # organagechronologicalpancreas
#
# Pancreas OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olin... |
c09a6d6868f13dee266a55dd6a310be6be18acfbadc36e2d1a42559940f7f8dd | Jupyter | 5,635 | 164 | # %% [markdown]
# In this notebook we will see the basics of how to use MiniSom.
#
# Let's start importing MiniSom:
# %%
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install minisom
LOCAL_DEV = True
if LOCAL_DEV:
sys.path.insert(0, '../')
%load_ext autoreload
# %%
from minisom im... |
d1ee8302af3bf4a8c7073d02856243c8e7c6407095422fce1612927ff96fb08b | Jupyter | 5,635 | 45 | # %% [markdown]
# # organagechronologicalesophagus
#
# Esophagus OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Ol... |
1f641caa24bc70dc30295c91b4577f7a212ef4fafce517fc476094e79d27945d | Jupyter | 5,636 | 45 | # %% [markdown]
# # organagechronologicalintestine
#
# Intestine OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Ol... |
6ff10423f4f92498a240229cb0c16fafbc6f9fee72150c5453eba26cf7699167 | Jupyter | 5,636 | 45 | # %% [markdown]
# # organagechronologicalpituitary
#
# Pituitary OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Ol... |
cf9b10f173443b743a36bd6f4e04c23a3ea458ab6590248a032da98b4401e3ec | Jupyter | 5,651 | 198 | # %% [markdown]
# # Using the NeuronBridge Python API
#
# This notebook demonstrates how to use the NeuronBridge Python API.
# %%
# Some boilerplate code to make the notebook work
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
#... |
d47e69246b3ea1d554cf638b88b75fa722b5a8aef26043ec8bc6273e4f0e5bfc | Jupyter | 5,653 | 45 | # %% [markdown]
# # organagechronologicalconventional
#
# Conventional OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs... |
7d0f6797e5874b2058ab5a038ff14f96c14693104147d9a05104fc734947c5d7 | Jupyter | 5,667 | 45 | # %% [markdown]
# # organagemortalityskin
#
# Skin OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs. Val... |
ef95d7914294283b26272b6375d2841dfa14b8966e7122cc1e916bf16bb8b30b | Jupyter | 5,667 | 45 | # %% [markdown]
# # organagemortalitylung
#
# Lung OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs. Val... |
7cb75a242d4b02ad43e1c46460e86da1094c935ad3fc90b6033e34098ecb8fcb | Jupyter | 5,671 | 45 | # %% [markdown]
# # organagemortalityheart
#
# Heart OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs. V... |
ef026af45d97363c87795ab2145eed1e59d747834e0056171661b57862a31190 | Jupyter | 5,672 | 45 | # %% [markdown]
# # organagemortalityliver
#
# Liver OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs. V... |
84b8cc19673da3eb87c02c2aee913c8470aae6a6dcfd772d153dc42d9d13a8b3 | Jupyter | 5,673 | 45 | # %% [markdown]
# # organagemortalitybrain
#
# Brain OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs. V... |
b08c2c10aadb9f2dd71ceb4d353102dab74faa61472bdfbe60b90c5503c951e6 | Jupyter | 5,673 | 189 | # %% [markdown]
# # DNAmTL
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-int... |
7864eaa4891b1836a082c3030010c72082784167f7861474b03b16ab08ae7d78 | Jupyter | 5,676 | 45 | # %% [markdown]
# # organagemortalitykidney
#
# Kidney OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs.... |
220daab9dc84bc218564a8518e47a8308baa48cc9ded1f95964db1c97a50f48c | Jupyter | 5,677 | 45 | # %% [markdown]
# # organagemortalitymuscle
#
# Muscle OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs.... |
95f32711f23f5e5a17c70e896428ebd94c69f858f250157554131f76bcc8bf1e | Jupyter | 5,677 | 45 | # %% [markdown]
# # organagemortalityartery
#
# Artery OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs.... |
d971485122ddc2ac42608ea0078f16a445e88ea192cd623dc6f2831339e0225e | Jupyter | 5,678 | 45 | # %% [markdown]
# # organagemortalityimmune
#
# Immune OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs.... |
0d8278ff78443d8667a8b6856d17dc161d01bc3bfe144d2819f9fdfecac97e88 | Jupyter | 5,681 | 45 | # %% [markdown]
# # organagemortalitythyroid
#
# Thyroid OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay ID... |
28f71540b8ffa5f8f58a77cc342bdc222a6fd831d9f810e56ba96844ead8e592 | Jupyter | 5,681 | 45 | # %% [markdown]
# # organagemortalityadipose
#
# Adipose OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay ID... |
89e30d3c99c279bce50c3e70b0c927dd1073430e98a2a5f22e8397caaaa890af | Jupyter | 5,681 | 45 | # %% [markdown]
# # organagemortalityadrenal
#
# Adrenal OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay ID... |
7cafd982b1a38f10e391fc42f0a7c0c3d334e0186bbec921db54296a4fb6ea8b | Jupyter | 5,682 | 45 | # %% [markdown]
# # organagemortalitystomach
#
# Stomach OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay ID... |
278af8eab4607d813a2046e9b1feb25f6887f1cba65707697842209bb381a255 | Jupyter | 5,687 | 45 | # %% [markdown]
# # organagemortalitysalivary
#
# Salivary OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay ... |
6230f1d409e88503557b37c026b076383fcd67fd62af2dc8872a5c7dcdbf4867 | Jupyter | 5,687 | 45 | # %% [markdown]
# # organagemortalitypancreas
#
# Pancreas OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay ... |
f5b5e4577afe1765071df385b60e47413825d93f14080f35d7c856c7cb7144ae | Jupyter | 5,691 | 45 | # %% [markdown]
# # organagemortalityesophagus
#
# Esophagus OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assa... |
3723158fa1a51a2e35a95a048a73c17f0b46a19dde3247d1f4ecbcf5d164e787 | Jupyter | 5,692 | 45 | # %% [markdown]
# # organagemortalitypituitary
#
# Pituitary OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assa... |
d31ecd0944a7408010e8136ae8621e03b55c10adc1e48cb8e7a7313721c7896d | Jupyter | 5,692 | 45 | # %% [markdown]
# # organagemortalityintestine
#
# Intestine OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assa... |
edb8b21e37d053cd08d5f54037777087c95ec08ee92db618f4524f3eba50bc94 | Jupyter | 5,707 | 189 | # %%
# Step 1: Load Data
from Bio import SeqIO
# Function to load data from a FASTA file
def load_fasta_file(file_path):
sequences = []
virus_types = []
for record in SeqIO.parse(file_path, "fasta"):
sequences.append(str(record.seq))
virus_type = record.description.split()[
0
... |
4504313fe1c7fa84658e864da38c9e8ef2ecd1fd88f15fa27c27fc617c15b8bb | Jupyter | 5,709 | 45 | # %% [markdown]
# # organagemortalityconventional
#
# Conventional OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olin... |
adf9143b28c56d7650024d8dfaac339432f5849765ed61860bd99a19b6defc57 | Jupyter | 5,718 | 215 | # %% [markdown]
# # Alignment handling and analysis using the Alignment class
#
# #### Contents:
# 1. Alignment input and output
#
# 2. Selecting rows and columns
#
# 3. Calculating statistics of alignments
#
# 4. Learning exercises to test your skills
#
# Authors: Anna G. Green, Thomas A. Hopf
#
# %%
import num... |
61694add79296d635153391f7f8e8dbdb7b347d9fb7699a9d915ee28a8ba2d93 | Jupyter | 5,723 | 166 | # %% [markdown]
# # Netherlands Neurogenetics Database
# Author: Nienke Mekkes <br>
# Date: 21-Sep-2022. <br>
# Correspond: n.j.mekkes@umcg.nl <br>
#
# ## Script: clinical history labeled training data: splitting data
# Objectives: load cleaned training data, split into <br>
# - test (will be stored separately)
# - tr... |
728ceb207cbfba99e1036f4543004d1af9553bce1b6dc7cb2f4fb88a47aa8002 | Jupyter | 5,729 | 176 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib
# %% [markdown]
# Initialize plotting
# %%
matplotlib.rcParams["svg.fonttype"] = "none"
plt.rcParams.updat... |
8c3d8dfb75cf072625a945ea5d70cf72af86bbc6588ca1da6978cb067ad261dc | Jupyter | 5,745 | 164 | # %%
#%% Transitive Inference - Model batch analysis #########################
import sys
import task_train_test as tk
import nn_analyses as nna
import behavior_functions as bf
import ti_functions as ti
#%% Select batch #######################################################################
# specify batch di... |
c45edefcc2f04165443c416c88d2f2a912f42b3ccfdf4c07e59f04607c91115a | Jupyter | 5,771 | 45 | # %% [markdown]
# # organagechronologicalmale
#
# Male OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay ... |
382f97017b7c4004014a65fb05d468ee34ee5f0c52d7a08cee4093dba636d39a | Jupyter | 5,779 | 188 | # %% [markdown]
# <h1 style="font-size: 20px;">In this notebook, we generate synthetic microbubble (MB) images using experimental MB signals</h1>
# %%
import os
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import torch
from torch.utils.data import DataLoader, Dataset
from mbset import sam... |
056c79debee3fccb74cc5eb6a0e387186f7c31c33b0af320960d4c4536fa6c15 | Jupyter | 5,780 | 45 | # %% [markdown]
# # organagechronologicalfemale
#
# Female OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink as... |
c882d8dd37fe275fb8fd19d1329ca7db6a76580cad91e9cd0354285dae596912 | Jupyter | 5,795 | 189 | # %% [markdown]
# # DNAmPhenoAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weigh... |
329ac6f78f26f2caf27091a7cedbd757d54943e0b7c341438d81aa3c1151189a | Jupyter | 5,803 | 192 | # %% [markdown]
# # ENCen40
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-in... |
b5f357136941440366e1d257072bc7960f963d6fad3affaae1ea3630364a0a5a | Jupyter | 5,803 | 45 | # %% [markdown]
# # organagechronologicalorganismal
#
# Organismal OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or ... |
41c3f3a5064ad6c73226cf4dd40ac81984f96c3e219f0b8092cd02930e9b25db | Jupyter | 5,804 | 45 | # %% [markdown]
# # organagechronologicalmultiorgan
#
# Multi-organ OrganAge chronological model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or... |
287f6a5e6e5c9ade848fc5b92e5f500921f8f8505b4f55d5cca065afa641dab4 | Jupyter | 5,808 | 192 | # %% [markdown]
# # Mammalian1
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights... |
f0e250ecf04d2f4926902b456b26dda4c345a7af9cb2a6e64c6a0c098ffaa8c0 | Jupyter | 5,820 | 204 | # %% [markdown]
# # Using the NeuronBridge Python API
#
# This notebook demonstrates how to use the NeuronBridge Python API.
#
# Please use the "notebooks" Pixi env to run this notebook. To get the interpreter path, you can run:
# ```
# pixi shell -e notebooks
# which python
# ```
# %%
# Some boilerplate code to mak... |
c6563473b68db504f4ba224f5417f65cdbeea19a3bcbd7bf730d045ec077da8e | Jupyter | 5,827 | 45 | # %% [markdown]
# # organagemortalitymale
#
# Male OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs. Val... |
17dde48924e14f6341eaed4149b7ccdfb51eb08f7d6173627c92ab45fa992e18 | Jupyter | 5,834 | 190 | # %% [markdown]
# # RetroelementAgeV1
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-... |
6576fd6c7d0b3123f54630c0fbd8ac71825af5178eebdae1c48a3aa014108900 | Jupyter | 5,836 | 45 | # %% [markdown]
# # organagemortalityfemale
#
# Female OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs.... |
0e06a2c1e33b376b18254b2bcdf32784bcc3de486f5e68c50b45aa87de2aade0 | Jupyter | 5,845 | 192 | # %% [markdown]
# # BiTAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-int... |
ae55153dd61f1b934753a37d47dc82bcef6fd84b2a95adb01bdb9d3f49b02b7e | Jupyter | 5,859 | 45 | # %% [markdown]
# # organagemortalityorganismal
#
# Organismal OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink as... |
bc5af50f7584e9fbb02d969421f1625a4f1e40b55b281f1e62fff890ddb11e51 | Jupyter | 5,860 | 45 | # %% [markdown]
# # organagemortalitymultiorgan
#
# Multi-organ OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink a... |
056906cb82bca4659b60199ea546c328f2a66ebead1f6a485e55fada878f3b06 | Jupyter | 5,872 | 190 | # %% [markdown]
# # RetroelementAgeV2
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-... |
6309b91b91c12db0aab5f5bbdff48d495ca23628c745e1aa3abd4a79219e20de | Jupyter | 5,875 | 192 | # %% [markdown]
# # ENCen100
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-i... |
43a0a69f571d6b523a8c0b11580607583f32a73a80c46e247c4f380ad55791be | Jupyter | 5,876 | 176 | # %% [markdown]
# In this notebook, we shall use a single-cell RNAseq data to estimate cell type proportions in spatial transcriptomics data.
#
# ## Datasets needed for tutorial
#
# The datasets used in this tutorial are available as part the [DISSECT](https://github.com/robinredX/DISSECT/tutorials/data) repository. ... |
1fd8602dd8f89ce84705f0bcec08dc808dd459893f268d12b9cab0d886df4d69 | Jupyter | 5,886 | 181 | # %% [markdown]
# # EpiCMITHyper
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weigh... |
9d03e06d011b52b4b30b04b8b7409569389639aefb9c83872ec37087444c9d6d | Jupyter | 5,890 | 170 | # %% [markdown]
# # We can look at the trajectories of both the weights and activity in phase space
# %%
import numpy as np
import numpy.lib.stride_tricks
import matplotlib.pyplot as plt
import scipy.linalg
import matplotlib.scale
plt.style.use('seaborn')
plt.rc('font',**{'family':'sans-serif','sans-serif':['Computer ... |
a7de12d1466779e7fc202391cb3ad58ed0b5fd26d919cb10e66405f87173553b | Jupyter | 5,892 | 181 | # %% [markdown]
# # Mayne
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into... |
16d90f4b2b6a8259c859f89225ede05b1888641bff7fdbb313a03e8a667de17f | Jupyter | 5,896 | 61 | # %% [markdown]
# # MiAge
#
# [Youn and Wang (2018)](https://doi.org/10.1080/15592294.2017.1389361) infer mitotic age from 268 CpGs using a nonlinear methylation-transmission model. The authors' `function_library.r` and fitted `site_specific_parameters.Rdata` are preserved in [this pinned archive mirror](https://githu... |
06b92d0905a7ae9aa109843f0b359dc9d6849148626ff39339cb52cf5c8ff0e8 | Jupyter | 5,899 | 178 | # %% [markdown]
# # DownSyndrome
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weigh... |
46b9c6de7aa494e8a3b2a5cc4419ca222ac0b99da420ebae44db2d08c2cf549c | Jupyter | 5,900 | 181 | # %% [markdown]
# # EpiCMITHypo
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weight... |
dbcd230607c431960964cd4b2a8d637e9cd249e8b89204d13599398fd3f0c0ac | Jupyter | 5,914 | 178 | # %% [markdown]
# # SenChronoAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weigh... |
3ab8c5899ea74fe2d4294d8a53269e9aa0b75751fdcb95b80ac94ebb90de8cf2 | Jupyter | 5,956 | 185 | # %% [markdown]
# # VidalBralo
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights... |
4fbff25b7ff70c1d07c2f4dce9d6cce58cb6b13ee1eafd8a73b2ad4200bfb414 | Jupyter | 5,961 | 188 | # %% [markdown]
# # DNAmFILi
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-i... |
ecf3d33a382262f27d271251959bc81fbce4c67f8b4f778c3e6c13ddc7955889 | Jupyter | 5,964 | 185 | # %% [markdown]
# # Hep
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into-b... |
975522a0b2103f9359a9029b94ba874655906af44fc01f4d9278f1f8ee4df848 | Jupyter | 5,979 | 186 | # %%
"""Plot STIM OFF vs. ON power (Figure 2A)."""
import os
import sys
from pathlib import Path
import numpy as np
import copy
import pandas as pd
import pte_stats
import pte_decode
from matplotlib import pyplot as plt
cd_path = Path(os.getcwd()).absolute().parent
sys.path.append(os.path.join(cd_path, "coherence"))
... |
5479abc0b32026670768a7b18343585259a6bf6c7f12d21f713bb155c5ca2f67 | Jupyter | 5,988 | 192 | # %% [markdown]
# # eABEC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into... |
fb1842b84c675ff14ed00b61d9d9e89a62a0a9fc9f773a17cbe1b7e8af399d0a | Jupyter | 5,992 | 192 | # %% [markdown]
# # ABEC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into-... |
dccc123a2489452acf5d0eb69c40d68761011c87ec6aa8bb3f17c436c2c16f52 | Jupyter | 6,003 | 183 | # %% [markdown]
# # EPICGA
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-int... |
00e9887e95b4288ddfa2088875b8e15bd44929ce15ca6bd4dc0cb485a9b81b7d | Jupyter | 6,006 | 195 | # %% [markdown]
# # PipekFilteredH
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
ea5f5060ca50207df15f37a24da934973f45080408a67d87ce0b6cd426c87d06 | Jupyter | 6,009 | 195 | # %% [markdown]
# # PipekElasticNet
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
e96a4e913aa8892378b452ce6a972a52d0c40b23f83da5a76074e787a77febef | Jupyter | 6,020 | 195 | # %% [markdown]
# # PipekRetrainedH
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
1710f17983896fdabb4255f30fba7da03ddb04ba56219c8ba8bb95fa5a044592 | Jupyter | 6,022 | 85 | # %%
import requests
import re
import xlrd
import glob
import csv
session = requests.Session()
textfiles=glob.glob('./*.xlsx')
for u in range(len(textfiles)):
sheet = xlrd.open_workbook(textfiles[u]).sheet_by_index(0)
samplenum=len(sheet.col_values(0))
with open(str(textfiles[u])+' Lipid Droplets.csv', 'w'... |
ca943346e44c024e5a08e9d6f6cb267fbd94b5ee6bb875a69fe91f5932de7dd3 | Jupyter | 6,022 | 200 | # %% [markdown]
# # Reg
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into-b... |
e3f5867c0168eafe50e1684b7ef1ef46d61e52ace2446a95c6ed244e7b2bd0a0 | Jupyter | 6,023 | 192 | # %% [markdown]
# # HRSInCHPhenoAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
161f067639e2ed1279ff47871c9b4603f86868ae587993bd6a6e18d8f82e7009 | Jupyter | 6,027 | 181 | # %% [markdown]
# # Garagnani
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-... |
61d56ae6f218243198d639da40c2af206f3b1b2438184f98073c78a8ea551336 | Jupyter | 6,037 | 191 | # %% [markdown]
# # Hannum
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-int... |
10f4218b07c60f942b73a6eedc5608a128047a38ec7afaeeca33e38587b60043 | Jupyter | 6,038 | 185 | # %% [markdown]
# # CTSLiver
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-i... |
e7a4b1e67a506c584bf7414266a5a506208fb5ba7dbeaa77bb797bd0c511ae0c | Jupyter | 6,038 | 157 | # %% [markdown]
# # Computing your own correlation matrix
#
# Velocity models (z-gain scores and thrivelines) need a correlation matrix (how strongly do z-scores correlate between two timepoints). In this example the timepoints are ages. In PCNtoolkit you can either load a precomputed correlation matrix (`CorrelationM... |
7063b95005f41b1c42239c2b2f3dda1477fcc82b498a0eab78cb85ff8944febd | Jupyter | 6,039 | 178 | # %% [markdown]
# # SenMortalityAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-we... |
937525ec5bafa7e665a62d88620ff8f2d760da5a38f85b489c040d8afe38ca54 | Jupyter | 6,049 | 192 | # %% [markdown]
# # PedBE
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into... |
396d476d294a1f0d9fcb58809a8ca5bb834407251e9cb6d377bbd6986f0ad5a0 | Jupyter | 6,061 | 181 | # %% [markdown]
# # ADBahadoSingh
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weig... |
b5ae16878f4693102518df7d61760ee05dc8d35bb875b017171b2b1bbdbce6e1 | Jupyter | 6,078 | 195 | # %%
import numpy as np
import matplotlib.pylab as plt
from numpy.fft import fft, ifft
#import seaborn as sns
import matplotlib as mpl
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams['text.usetex'] = True # for using Times New Roman fonts
plt.rc('text.latex', preamble=r'\usepackage{amsmath,dsfont}') #to... |
2a14d8bb2d235169af8f1d2099c851542525cb3440d1a408d7baebdcdf711ad9 | Jupyter | 6,080 | 181 | # %% [markdown]
# # Bocklandt
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-... |
227c7cad52ec2459f21a2c4a0834736bb3472db4dc85c6080103009775272563 | Jupyter | 6,105 | 219 | # %%
import os
import sys
sys.path.append("..") # Adds higher directory to python modules path.
from src import RASPRoutines
import polars as pl
import pandas as pd
import numpy as np
from src import IOFunctions
from src import AnalysisFunctions
RASP = RASPRoutines.RASP_Routines()
IO = IOFunctions.IO_Functions()
A_F... |
5048193a801a8443309e45ddbee69a9b2db8f2c69c6a168cfb44670b110398c3 | Jupyter | 6,112 | 181 | # %% [markdown]
# # Wu
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into-ba... |
aff21d50cc9419b3fee716ca508df2991b3fd7124cf0d3141ada5a6977c7284d | Jupyter | 6,112 | 218 | # %%
import os.path as op
from surfplot.utils import add_fslr_medial_wall
from neuromaps.datasets import fetch_fslr
from sklearn.cluster import KMeans
from surfplot import Plot
from sklearn.metrics import pairwise_distances
from gradec.decode import LDADecoder
from gradec.utils import _rm_medial_wall, _decoding_filter... |
e5bba2e0b6ff738c38a2896ede1bff4b4c3d6aa0cf6e42d7183420f30a01cca8 | Jupyter | 6,112 | 224 | # %% [markdown]
# # Import qbiome
# %%
import qbiome
from qbiome.data_formatter import DataFormatter
from qbiome.quantizer import Quantizer
from qbiome.qnet_orchestrator import QnetOrchestrator
from qbiome.mask_checker import MaskChecker
from qbiome.forecaster import Forecaster
from qbiome.hypothesis import Hypothesis... |
2c038daeca87e600b22cdf3990bad1559ed55f102b6d1b966bbf7a6388ec5e18 | Jupyter | 6,118 | 192 | # %% [markdown]
# # cABEC
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into... |
f42530826c4497ac4efa7e06d78b0aefd03f1317d15c13fcd9f64ea2cb6eb398 | Jupyter | 6,120 | 176 | # %%
import os
import matplotlib.pyplot as plt
from matplotlib.streamplot import Grid
if os.environ.get('DISPLAY') is not None:
%matplotlib tk
import msmfcode_src
from msmfcode.core.config import *
from msmfcode.models.cann import DMSMF, FMSMF, Grid, SSSF
from msmfcode.evaluation.plot import gen_plot_error_box
... |
6816c1b121c866627ad6cf9823c1a4445aa591ea1e597f0aabb507baccce5d2b | Jupyter | 6,121 | 175 | # %% [markdown]
# # EpiTOC3
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-in... |
512c5957bb29bd3bf808685ae1765b0b5d5d04a85f34d367a5d09e0bc94abcb9 | Jupyter | 6,122 | 182 | # %% [markdown]
# # StemTOCvitro
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weigh... |
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