Add YAML frontmatter, quick-start code, fix author, 4-domain scope

README.md

@@ -1,3 +1,38 @@
+---
+license: cc-by-sa-4.0
+task_categories:
+  - text-classification
+  - text-generation
+  - tabular-classification
+language:
+  - en
+size_categories:
+  - 10M<n<100M
+tags:
+  - biology
+  - chemistry
+  - drug-discovery
+  - clinical-trials
+  - protein-protein-interaction
+  - gene-essentiality
+  - negative-results
+  - publication-bias
+  - benchmark
+  - biomedical
+pretty_name: "NegBioDB: Negative Results Database & Benchmark"
+configs:
+  - config_name: dti_pairs
+    data_files: "data/negbiodb_dti_pairs.parquet"
+  - config_name: dti_m1_balanced
+    data_files: "data/negbiodb_m1_balanced.parquet"
+  - config_name: ct_pairs
+    data_files: "data/ct/negbiodb_ct_pairs.parquet"
+  - config_name: ppi_pairs
+    data_files: "data/ppi/negbiodb_ppi_pairs.parquet"
+  - config_name: ge_pairs
+    data_files: "data/ge/negbiodb_ge_pairs.parquet"
+---
+
 # NegBioDB
 
 **Negative Results Database & Dual ML/LLM Benchmark for Biomedical Sciences**
@@ -7,15 +42,36 @@
 
 Approximately 90% of scientific experiments produce null or inconclusive results, yet the vast majority remain unpublished. NegBioDB systematically collects experimentally confirmed negative results across four biomedical domains and provides dual-track ML + LLM benchmarks to quantify the impact of this publication bias on AI models.
 
-## Key Features
+## Quick Start
+
+```python
+# Load any domain with the datasets library
+from datasets import load_dataset
 
-- **Four domains**: Drug-Target Interaction (DTI), Clinical Trial Failure (CT), Protein-Protein Interaction (PPI), Gene Essentiality (GE/DepMap)
-- **~61.6M negative results** across 4 SQLite databases (30.5M DTI + 133K CT + 2.2M PPI + 28.8M GE)
-- **Dual benchmark**: ML track (traditional prediction) + LLM track (biomedical NLP tasks)
-- **242 ML runs** + **321 LLM runs** completed across all domains
-- **Multiple split strategies**: random, cold-entity, temporal, scaffold, degree-balanced
-- **Reproducible pipeline**: SQLite databases, config-driven ETL, SLURM/HPC support
-- **Standardized evaluation**: 7 ML metrics (LogAUC, BEDROC, EF, AUROC, AUPRC, MCC) + LLM rubrics
+# DTI pairs (30.5M negative compound-target interactions)
+dti = load_dataset("jang1563/NegBioDB", "dti_pairs", split="train")
+
+# Clinical trial failures (102K intervention-condition pairs)
+ct = load_dataset("jang1563/NegBioDB", "ct_pairs", split="train")
+
+# PPI negatives (2.2M confirmed non-interactions)
+ppi = load_dataset("jang1563/NegBioDB", "ppi_pairs", split="train")
+
+# Gene essentiality (22.5M gene-cell-line pairs)
+ge = load_dataset("jang1563/NegBioDB", "ge_pairs", split="train")
+```
+
+Or load directly with pandas:
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+# Download a specific file
+path = hf_hub_download("jang1563/NegBioDB", "data/negbiodb_dti_pairs.parquet", repo_type="dataset")
+df = pd.read_parquet(path)
+print(df.shape)  # (1_725_446, ~20 columns)
+```
 
 ## Database Statistics
 
@@ -27,26 +83,11 @@ Approximately 90% of scientific experiments produce null or inconclusive results
 | **GE** | 28,759,256 | 19,554 genes, 2,132 cell lines | DepMap (CRISPR, RNAi) | ~16 GB |
 | **Total** | **~61.6M** | | **14 sources** | **~38 GB** |
 
-*PPI DB total: 2,229,670; export rows after split filtering: 2,220,786.*
-
-## Project Status
-
-| Domain | ETL | ML Benchmark | LLM Benchmark | Status |
-|--------|-----|-------------|---------------|--------|
-| DTI | 4 sources | 24/24 runs | 81/81 runs | Complete |
-| CT | 4 sources | 108/108 runs | 80/80 runs | Complete |
-| PPI | 4 sources | 54/54 runs | 80/80 runs | Complete |
-| GE | 2 sources | 14/14 runs (seed 42) | 64/80 runs* | Seed 42 ML complete, LLM 4/5 models |
-
-*Llama 3.1-8B results pending HPC GPU availability; seeds 43/44 in progress.
-
----
-
 ## Key Findings
 
 ### ML: Negative Source Matters
 
-**DTI** — Degree-matched negatives inflate LogAUC by +0.112 on average. Cold-target splits cause catastrophic failure (LogAUC 0.15–0.33), while AUROC misleadingly stays 0.76–0.89.
+**DTI** -- Degree-matched negatives inflate LogAUC by +0.112 on average. Cold-target splits cause catastrophic failure:
 
 | DTI Model | Random (NegBioDB) | Random (Degree-Matched) | Cold-Target |
 |-----------|------------------|------------------------|-------------|
@@ -54,339 +95,100 @@ Approximately 90% of scientific experiments produce null or inconclusive results
 | GraphDTA | 0.843 | **0.967** | 0.241 |
 | DrugBAN | 0.830 | **0.955** | 0.151 |
 
-**PPI** — PIPR cold_both AUROC drops to 0.409 (below random); MLPFeatures remains robust at 0.950 due to hand-crafted features.
+**PPI** -- PIPR cold_both AUROC drops to 0.409 (below random); MLPFeatures remains robust at 0.950.
 
-**CT** — NegBioDB negatives are trivially separable (AUROC ~1.0); M2 7-way classification is challenging (best macro-F1 = 0.51).
+**CT** -- NegBioDB negatives are trivially separable (AUROC ~1.0); M2 7-way classification is challenging (best macro-F1 = 0.51).
 
-**GE** — Cold-gene splits reveal severe generalization gaps; degree-balanced negatives modestly improve ranking metrics over random negatives.
+**GE** -- Cold-gene splits reveal severe generalization gaps; degree-balanced negatives modestly improve ranking metrics.
 
 ### LLM: L4 Discrimination Reveals Domain Differences
 
 | Domain | L4 MCC Range | Interpretation | Contamination |
 |--------|-------------|----------------|---------------|
-| DTI | ≤ 0.18 | Near random | Not detected |
-| PPI | 0.33–0.44 | Moderate | **Yes** (temporal gap) |
-| CT | 0.48–0.56 | Meaningful | Not detected |
-| GE | Pending full run | — | — |
-
-PPI L4 reveals **temporal contamination**: pre-2015 interaction data is identified at 59–79% accuracy, while post-2020 data drops to 7–25%. LLMs rely on memorized training data, not biological reasoning.
-
----
-
-## Setup
-
-Requires Python 3.11+ and [uv](https://docs.astral.sh/uv/).
-
-```bash
-git clone https://github.com/jang1563/NegBioDB.git
-cd NegBioDB
-make setup          # Create venv and install dependencies
-make db             # Initialize SQLite database
-```
-
-## Data Pipeline
-
-### DTI Domain
-
-```bash
-make download       # Download all 4 sources (ChEMBL, PubChem, BindingDB, DAVIS)
-make load-all       # Run all ETL loaders
-uv run python scripts/export_ml_dataset.py   # Export ML datasets
-```
-
-### CT Domain
-
-```bash
-# Download sources (AACT URL changes monthly)
-uv run python scripts_ct/download_aact.py --url <AACT_URL>
-uv run python scripts_ct/download_cto.py
-uv run python scripts_ct/download_opentargets.py
-uv run python scripts_ct/download_shi_du.py
-
-# Load and process
-uv run python scripts_ct/load_aact.py
-uv run python scripts_ct/classify_failures.py
-uv run python scripts_ct/resolve_drugs.py
-uv run python scripts_ct/load_outcomes.py
-uv run python scripts_ct/export_ct_ml_dataset.py
-```
-
-### PPI Domain
-
-```bash
-# Download sources
-uv run python scripts_ppi/download_intact.py
-uv run python scripts_ppi/download_huri.py
-uv run python scripts_ppi/download_humap.py
-uv run python scripts_ppi/download_string.py
-
-# Load and process
-uv run python scripts_ppi/load_intact.py
-uv run python scripts_ppi/load_huri.py
-uv run python scripts_ppi/load_humap.py
-uv run python scripts_ppi/load_string.py
-uv run python scripts_ppi/fetch_sequences.py
-uv run python scripts_ppi/export_ppi_ml_dataset.py
-```
-
-### GE Domain (DepMap)
-
-```bash
-# Download DepMap CRISPR and RNAi screens
-uv run python scripts_depmap/download_depmap.py
-
-# Load and process
-uv run python scripts_depmap/load_depmap.py
-uv run python scripts_depmap/load_rnai.py
-uv run python scripts_depmap/fetch_gene_descriptions.py
-uv run python scripts_depmap/export_ge_ml_dataset.py
-```
-
-## ML Experiments
-
-```bash
-# DTI training (local or SLURM)
-uv run python scripts/train_baseline.py --model deepdta --split random --negative negbiodb --dataset balanced
-bash slurm/submit_all.sh
-
-# CT training
-uv run python scripts_ct/train_ct_baseline.py --model xgboost --task m1 --split random --negative negbiodb
-bash slurm/submit_ct_all.sh
-
-# PPI training
-uv run python scripts_ppi/train_baseline.py --model siamese_cnn --split random --negative negbiodb --dataset balanced
-bash slurm/submit_ppi_all.sh
-
-# GE training
-uv run python scripts_depmap/train_ge_baseline.py --model xgboost --split random --negative negbiodb
-bash slurm/submit_ge_ml_all.sh
-
-# Results collection (all domains support --aggregate-seeds)
-uv run python scripts/collect_results.py --dataset balanced --aggregate-seeds
-uv run python scripts_ct/collect_ct_results.py --aggregate-seeds
-uv run python scripts_ppi/collect_results.py --dataset balanced --aggregate-seeds
-uv run python scripts_depmap/collect_ge_results.py --aggregate-seeds
-```
-
-## LLM Benchmark
-
-```bash
-# Build LLM datasets (example: DTI)
-uv run python scripts/build_l1_dataset.py
-uv run python scripts/build_l2_dataset.py
-uv run python scripts/build_l3_dataset.py
-uv run python scripts/build_l4_dataset.py
-
-# Run LLM inference
-uv run python scripts/run_llm_benchmark.py --model gemini --level l1 --config zeroshot
-
-# GE-specific LLM datasets and inference
-uv run python scripts_depmap/build_ge_l1_dataset.py
-uv run python scripts_depmap/run_ge_llm_benchmark.py --model gemini --level l1 --config zeroshot
-
-# Collect results
-uv run python scripts/collect_llm_results.py
-uv run python scripts_ct/collect_ct_llm_results.py
-uv run python scripts_ppi/collect_ppi_llm_results.py
-uv run python scripts_depmap/collect_ge_results.py --llm
-```
-
-## Testing
-
-```bash
-# All tests (~1,000 total across 4 domains)
-PYTHONPATH=src uv run pytest tests/ -v
-
-# By domain
-PYTHONPATH=src uv run pytest tests/test_db.py tests/test_etl_*.py tests/test_export.py -v       # DTI
-PYTHONPATH=src uv run pytest tests/test_ct_*.py tests/test_etl_aact.py -v                       # CT
-PYTHONPATH=src uv run pytest tests/test_ppi_*.py tests/test_etl_intact.py -v                    # PPI
-PYTHONPATH=src uv run pytest tests/test_ge_*.py tests/test_etl_depmap.py -v                     # GE
-
-# Skip network-dependent tests
-PYTHONPATH=src uv run pytest tests/ -v -m "not integration"
-```
-
-## Project Structure
-
-```
-NegBioDB/
-├── src/
-│   ├── negbiodb/              # DTI core library
-│   │   ├── db.py              # Database creation & migrations
-│   │   ├── download.py        # Download utilities (resume, checksum)
-│   │   ├── standardize.py     # Compound/target standardization (RDKit)
-│   │   ├── etl_davis.py       # DAVIS ETL pipeline
-│   │   ├── etl_chembl.py      # ChEMBL ETL pipeline
-│   │   ├── etl_pubchem.py     # PubChem ETL (streaming, 29M rows)
-│   │   ├── etl_bindingdb.py   # BindingDB ETL pipeline
-│   │   ├── export.py          # ML dataset export (Parquet, 5 splits)
-│   │   ├── metrics.py         # ML evaluation metrics (7 metrics)
-│   │   ├── llm_client.py      # LLM API client (vLLM, Gemini, OpenAI, Anthropic)
-│   │   ├── llm_prompts.py     # LLM prompt templates (L1-L4)
-│   │   ├── llm_eval.py        # LLM evaluation functions
-│   │   └── models/            # ML baseline models
-│   │       ├── deepdta.py     # DeepDTA (sequence CNN)
-│   │       ├── graphdta.py    # GraphDTA (graph neural network)
-│   │       └── drugban.py     # DrugBAN (bilinear attention)
-│   ├── negbiodb_ct/           # Clinical Trial domain
-│   │   ├── ct_db.py           # CT database & migrations
-│   │   ├── etl_aact.py        # AACT ETL (13 tables)
-│   │   ├── etl_classify.py    # 3-tier failure classification
-│   │   ├── drug_resolver.py   # 4-step drug name resolution
-│   │   ├── etl_outcomes.py    # Outcome enrichment (p-values, SAE)
-│   │   ├── ct_export.py       # ML export (M1/M2, 6 splits)
-│   │   ├── ct_features.py     # Feature encoding (1044/1066-dim)
-│   │   ├── ct_models.py       # CT_MLP, CT_GNN_Tab models
-│   │   ├── llm_prompts.py     # CT LLM prompts (L1-L4)
-│   │   ├── llm_eval.py        # CT LLM evaluation
-│   │   └── llm_dataset.py     # CT LLM dataset construction
-│   ├── negbiodb_ppi/          # PPI domain
-│   │   ├── ppi_db.py          # PPI database & migrations
-│   │   ├── etl_intact.py      # IntAct PSI-MI TAB 2.7
-│   │   ├── etl_huri.py        # HuRI Y2H screen negatives
-│   │   ├── etl_humap.py       # hu.MAP ML-derived negatives
-│   │   ├── etl_string.py      # STRING zero-score pairs
-│   │   ├── protein_mapper.py  # UniProt validation, ENSG mapping
-│   │   ├── export.py          # ML export (4 splits, controls)
-│   │   ├── llm_prompts.py     # PPI LLM prompts (L1-L4)
-│   │   ├── llm_eval.py        # PPI LLM evaluation
-│   │   ├── llm_dataset.py     # PPI LLM dataset construction
-│   │   └── models/            # PPI ML models
-│   │       ├── siamese_cnn.py # Shared CNN encoder
-│   │       ├── pipr.py        # Cross-attention PPI model
-│   │       └── mlp_features.py # Hand-crafted feature MLP
-│   └── negbiodb_depmap/       # Gene Essentiality (DepMap) domain
-│       ├── depmap_db.py       # GE database & migrations
-│       ├── etl_depmap.py      # DepMap CRISPR ETL
-│       ├── etl_rnai.py        # RNAi screen ETL
-│       ├── etl_prism.py       # PRISM drug screen ETL (optional)
-│       ├── export.py          # ML export (5 splits, 770 MB parquet)
-│       ├── ge_features.py     # Gene/cell-line feature encoding
-│       ├── llm_prompts.py     # GE LLM prompts (L1-L4)
-│       ├── llm_eval.py        # GE LLM evaluation
-│       └── llm_dataset.py     # GE LLM dataset construction
-├── scripts/                   # DTI CLI entry points
-├── scripts_ct/                # CT CLI entry points
-├── scripts_ppi/               # PPI CLI entry points
-├── scripts_depmap/            # GE CLI entry points
-├── slurm/                     # SLURM job scripts (HPC-ready, path-agnostic)
-├── migrations/                # DTI SQL schema migrations
-├── migrations_ct/             # CT SQL schema migrations
-├── migrations_ppi/            # PPI SQL schema migrations
-├── migrations_depmap/         # GE SQL schema migrations
-├── tests/                     # Test suite (~1,000 tests across 4 domains)
-├── docs/                      # Methodology notes and prompt appendices
-├── paper/                     # LaTeX source (NeurIPS 2026 submission)
-├── data/                      # SQLite databases (not in repo, ~38 GB)
-├── exports/                   # ML/LLM export files (Parquet, not in repo)
-├── results/                   # Experiment results (not in repo)
-├── config.yaml                # Pipeline configuration
-├── Makefile                   # Build/pipeline commands
-├── pyproject.toml             # Python project metadata
-├── experiment_results.md      # ML/LLM result tables (all 4 domains)
-├── PROJECT_OVERVIEW.md        # Detailed project overview
-└── ROADMAP.md                 # Execution roadmap
-```
+| DTI | &le; 0.18 | Near random | Not detected |
+| PPI | 0.33--0.44 | Moderate | **Yes** (temporal gap) |
+| CT | 0.48--0.56 | Meaningful | Not detected |
+| GE | Pending | -- | -- |
 
 ## Exported Datasets
 
-### DTI (`exports/`)
+### DTI
 
 | File | Description |
 |------|-------------|
 | `negbiodb_dti_pairs.parquet` | 1.7M compound-target pairs with 5 split columns |
 | `negbiodb_m1_balanced.parquet` | M1: 1.73M rows (1:1 active:inactive) |
 | `negbiodb_m1_realistic.parquet` | M1: 9.49M rows (1:10 ratio) |
-| `negbiodb_m1_balanced_ddb.parquet` | Exp 4: degree-balanced split |
-| `negbiodb_m1_uniform_random.parquet` | Exp 1: uniform random negatives |
-| `negbiodb_m1_degree_matched.parquet` | Exp 1: degree-matched negatives |
-| `chembl_positives_pchembl6.parquet` | 863K ChEMBL actives (pChEMBL >= 6) |
-| `compound_names.parquet` | 144K compound names (for LLM tasks) |
+| `negbiodb_m1_balanced_ddb.parquet` | Degree-balanced split |
+| `negbiodb_m1_uniform_random.parquet` | Uniform random negatives (control) |
+| `negbiodb_m1_degree_matched.parquet` | Degree-matched negatives (control) |
+
+### CT
+
+| File | Description |
+|------|-------------|
+| `ct/negbiodb_ct_pairs.parquet` | 102,850 failure pairs, 6 splits |
+| `ct/negbiodb_ct_m1_balanced.parquet` | Binary: 11,222 rows (5,611 pos + 5,611 neg) |
+| `ct/negbiodb_ct_m2.parquet` | 7-way category: 112,298 rows |
 
-### CT (`exports/ct/`)
+### PPI
 
 | File | Description |
 |------|-------------|
-| `negbiodb_ct_pairs.parquet` | 102,850 failure pairs, 6 splits, all tiers |
-| `negbiodb_ct_m1_balanced.parquet` | Binary: 11,222 rows (5,611 pos + 5,611 neg) |
-| `negbiodb_ct_m1_realistic.parquet` | Binary: 36,957 rows (1:~6 ratio) |
-| `negbiodb_ct_m1_smiles_only.parquet` | Binary: 3,878 rows (SMILES-resolved only) |
-| `negbiodb_ct_m2.parquet` | 7-way category: 112,298 rows (non-copper) |
+| `ppi/negbiodb_ppi_pairs.parquet` | 2,220,786 negative pairs with split columns |
+| `ppi/ppi_m1_balanced.parquet` | M1: 123,456 rows (1:1 pos:neg) |
+| `ppi/ppi_m1_realistic.parquet` | M1: 679,008 rows (1:10 ratio) |
 
-### PPI (`exports/ppi/`)
+### GE
 
 | File | Description |
 |------|-------------|
-| `negbiodb_ppi_pairs.parquet` | 2,220,786 negative pairs with split columns |
-| `ppi_m1_balanced.parquet` | M1: 123,456 rows (1:1 pos:neg) |
-| `ppi_m1_realistic.parquet` | M1: 679,008 rows (1:10 ratio) |
-| `ppi_m1_balanced_ddb.parquet` | Exp 4: degree-balanced split |
-| `ppi_m1_uniform_random.parquet` | Exp 1: uniform random negatives |
-| `ppi_m1_degree_matched.parquet` | Exp 1: degree-matched negatives |
+| `ge/negbiodb_ge_pairs.parquet` | 22.5M gene-cell-line pairs with 5 split columns |
 
-### GE (`exports/ge/`)
+### LLM Benchmarks
 
 | File | Description |
 |------|-------------|
-| `negbiodb_ge_pairs.parquet` | 770 MB; 22.5M gene-cell-line pairs with 5 split columns |
-| `ge_m1_random.parquet` | Random split (train/val/test) |
-| `ge_m1_cold_gene.parquet` | Cold-gene generalization split |
-| `ge_m1_cold_cell_line.parquet` | Cold-cell-line generalization split |
-| `ge_m1_cold_both.parquet` | Cold-both (hardest) split |
-| `ge_m1_degree_balanced.parquet` | Degree-balanced negative control |
+| `llm_benchmarks/l1_mcq.jsonl` | L1: Multiple-choice classification |
+| `llm_benchmarks/l4_tested_untested.jsonl` | L4: Tested vs. untested discrimination |
+| `ct_llm/ct_l*_dataset.jsonl` | CT domain LLM datasets (L1-L4) |
+| `ppi_llm/ppi_l*_dataset.jsonl` | PPI domain LLM datasets (L1-L4) |
+| `ge_llm/ge_l*_dataset.jsonl` | GE domain LLM datasets (L1-L4) |
 
 ## Data Sources
 
-### DTI
-
 | Source | Records | License |
 |--------|---------|---------|
 | [ChEMBL v36](https://www.ebi.ac.uk/chembl/) | 371K | CC BY-SA 3.0 |
 | [PubChem BioAssay](https://pubchem.ncbi.nlm.nih.gov/) | 29.6M | Public Domain |
 | [BindingDB](https://www.bindingdb.org/) | 404K | CC BY |
 | [DAVIS](https://github.com/dingyan20/Davis-Dataset-for-DTA-Prediction) | 20K | Public |
-
-### CT
-
-| Source | Records | License |
-|--------|---------|---------|
 | [AACT (ClinicalTrials.gov)](https://aact.ctti-clinicaltrials.org/) | 216,987 trials | Public Domain |
 | [CTO](https://github.com/fairnessforensics/CTO) | 20,627 | MIT |
 | [Open Targets](https://www.opentargets.org/) | 32,782 targets | Apache 2.0 |
 | [Shi & Du 2024](https://doi.org/10.1038/s41597-024-03399-2) | 119K + 803K rows | CC BY 4.0 |
-
-### PPI
-
-| Source | Records | License |
-|--------|---------|---------|
 | [IntAct](https://www.ebi.ac.uk/intact/) | 779 pairs | CC BY 4.0 |
 | [HuRI](http://www.interactome-atlas.org/) | 500,000 pairs | CC BY 4.0 |
 | [hu.MAP 3.0](https://humap3.proteincomplexes.org/) | 1,228,891 pairs | MIT |
 | [STRING v12.0](https://string-db.org/) | 500,000 pairs | CC BY 4.0 |
+| [DepMap CRISPR](https://depmap.org/) | 28.7M gene-cell pairs | CC BY 4.0 |
+| [DepMap RNAi](https://depmap.org/) | Integrated | CC BY 4.0 |
 
-### GE
+## Reproducing from Source
 
-| Source | Records | License |
-|--------|---------|---------|
-| [DepMap CRISPR (Chronos)](https://depmap.org/) | 28.7M gene-cell pairs | CC BY 4.0 |
-| [DepMap RNAi (DEMETER2)](https://depmap.org/) | Integrated | CC BY 4.0 |
+Full pipeline code is available at [GitHub](https://github.com/jang1563/NegBioDB).
 
-## ML Evaluation Metrics
-
-| Metric | Role |
-|--------|------|
-| **LogAUC[0.001,0.1]** | Primary ranking metric (early enrichment) |
-| **BEDROC (alpha=20)** | Early enrichment (exponentially weighted) |
-| **EF@1%, EF@5%** | Enrichment factor at top 1%/5% |
-| **AUPRC** | Secondary ranking metric |
-| **MCC** | Balanced classification |
-| **AUROC** | Backward compatibility |
+```bash
+git clone https://github.com/jang1563/NegBioDB.git
+cd NegBioDB
+make setup          # Create venv and install dependencies
+make db             # Initialize SQLite database
+make download       # Download all sources
+make load-all       # Run ETL pipelines
+```
 
 ## Citation
 
-If you use NegBioDB in your research, please cite:
-
 ```bibtex
 @misc{negbiodb2026,
   title={NegBioDB: A Negative Results Database and Dual ML/LLM Benchmark for Biomedical Sciences},
@@ -396,14 +198,8 @@ If you use NegBioDB in your research, please cite:
 }
 ```
 
-## License
-
-**CC BY-SA 4.0** — see [LICENSE](LICENSE) for details.
-
-This license is required by the viral clause in ChEMBL's CC BY-SA 3.0 license.
-
----
+## License & Contact
 
-## Contact
+**License:** CC BY-SA 4.0 (required by ChEMBL's CC BY-SA 3.0 viral clause)
 
-JangKeun Kim (jak4013@med.cornell.edu) — Weill Cornell Medicine
+**Contact:** JangKeun Kim (jak4013@med.cornell.edu) -- Weill Cornell Medicine