Update README.md

README.md

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----
-license: mit
----
+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- mitochondrial-variants
+- pathogenicity-prediction
+- protein-language-model
+- ESM-2
+- machine-learning
+pretty_name: IDP Pathogenicity Model
+size_categories:
+- 10K<n<100K
+---
+Mechanistically Informed Machine Learning for Pathogenicity Prediction of Mitochondrial Missense Variants
+
+Harrizi S., Nait Idraha I., Mostafa K., Arnoult D.
+Submitted to Bioinformatics (Oxford University Press)
+
+
+Overview
+This repository contains the full analysis pipeline for a hybrid machine learning framework that predicts the pathogenicity of missense variants in mitochondrial proteins. The model integrates ESM-2 protein language model embeddings with 45 interpretable biophysical features tailored to mitochondrial biology, trained and validated on 11,928 ClinVar-annotated variants spanning 639 mitochondrial proteins.
+Key results:
+ModelAUC–ROCAUC–PROur model (MLP + ESM-2)0.8990.923Random Forest + ESM-20.882—Logistic Regression + ESM-20.864—AlphaMissense0.9420.954PolyPhen-20.8450.799SIFT0.8260.749
+All models evaluated under leave-protein-out cross-validation on the same variant set.
+
+Repository Structure
+IDP/
+│
+├── data/
+│   ├── raw/                        # Downloaded source data
+│   │   ├── clinvar/                # ClinVar variant annotations (release 2024/12)
+│   │   ├── uniprot/                # UniProt mitochondrial protein sequences
+│   │   ├── disprot/                # DisProt intrinsic disorder annotations
+│   │   ├── mobidb/                 # MobiDB disorder predictions
+│   │   ├── alphafold/              # AlphaFold structure data
+│   │   ├── dms/                    # Deep mutational scanning data
+│   │   └── llpsdb/                 # Liquid-liquid phase separation database
+│   │
+│   ├── processed/                  # Cleaned and merged datasets
+│   │   ├── mutation_dataset.parquet
+│   │   ├── mutation_features.parquet
+│   │   ├── mutations_dataset_final.parquet / .tsv
+│   │   └── mutations_dataset_mito_strict.parquet / .tsv
+│   │
+│   └── frozen/                     # MD5-versioned frozen dataset for reproducibility
+│       ├── mutations_dataset_final_FROZEN.parquet
+│       ├── mutations_dataset_mito_strict_FROZEN.parquet
+│       └── FREEZE_METADATA.json
+│
+├── embeddings/                     # ESM-2 embeddings (precomputed)
+│   ├── embeddings_combined_full.npy      # Global + local combined (full dataset)
+│   ├── embeddings_global_full.npy        # Global protein embeddings
+│   ├── embeddings_local_full.npy         # Local ±15-residue window embeddings
+│   ├── embeddings_combined_strict.npy    # Strict mitochondrial subset
+│   └── embeddings_by_protein.pkl         # Per-protein embedding dictionary
+│
+├── features/                       # Extracted biophysical features
+│   ├── features_classical_full.parquet
+│   └── features_classical_mito_strict.parquet
+│
+├── models/                         # Trained model checkpoints
+│   ├── checkpoints/
+│   │   ├── pathogenicity_classifier.pkl
+│   │   └── pathogenicity_classifier_v2.pkl
+│   ├── model_classical_baseline.pkl
+│   └── model_classical_mito_strict.pkl
+│
+├── results/                        # Outputs and evaluations
+│   ├── evaluations/
+│   │   └── lpocv_results.json
+│   ├── lpocv_predictions_final.parquet
+│   ├── hierarchical_validation_final.pkl
+│   ├── full_analysis_with_ensemble.parquet
+│   └── feature_importances_classical.csv
+│
+├── scripts/                        # Python analysis scripts
+│   ├── data_download.py
+│   ├── build_clinvar_mito_dataset.py
+│   ├── build_mutation_dataset.py
+│   ├── phase1_freeze_and_classical_features.py
+│   ├── esm2_t33_650M_UR50D.py
+│   ├── train_baseline_classical_model.py
+│   ├── final_mlp_embedding_model.py
+│   ├── lpocv_validation.py
+│   ├── hierarchical_validation_no_leakage.py
+│   ├── model_comparison_features_vs_esm2.py
+│   └── analyze_bias_and_train_strict_mito.py
+│
+└── configs/                        # Configuration files
+
+Installation
+bashgit clone https://github.com/YOUR_USERNAME/IDP-Pathogenicity-Model.git
+cd IDP-Pathogenicity-Model
+
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+pip install fair-esm
+pip install numpy scipy pandas scikit-learn statsmodels
+pip install biopython matplotlib seaborn plotly
+pip install einops tqdm joblib requests pyyaml networkx pyarrow
+Python version: 3.10
+Key dependencies: PyTorch 2.0 · scikit-learn 1.3 · NumPy 1.24 · pandas 2.0 · fair-esm
+GPU: NVIDIA A100 recommended (Google Colab Pro+ used for training)
+
+Reproducing the Analysis
+1. Download raw data
+bashpython scripts/data_download.py
+Downloads ClinVar (release 2024/12), UniProt mitochondrial proteins, DisProt, and MobiDB. Expected runtime: ~30 minutes.
+2. Build the variant dataset
+bashpython scripts/build_clinvar_mito_dataset.py
+python scripts/build_mutation_dataset.py
+Filters variants to mitochondrial proteins using MitoCarta 3.0 and OMIM annotations.
+3. Extract biophysical features
+bashpython scripts/phase1_freeze_and_classical_features.py
+Computes 45 biophysical features per variant.
+4. Extract ESM-2 embeddings
+bashpython scripts/esm2_t33_650M_UR50D.py
+Generates global and local (±15-residue window) embeddings using esm2_t33_650M_UR50D. For proteins longer than 1,022 residues, embeddings are computed on the N-terminal portion. Requires GPU. Expected runtime: ~2–4 hours.
+5. Train and validate the model
+bashpython scripts/final_mlp_embedding_model.py
+python scripts/lpocv_validation.py
+Trains the MLP classifier and runs leave-protein-out cross-validation. StandardScaler and PCA are fitted exclusively on the training fold — no test-set leakage.
+6. Baseline model comparison
+bashpython scripts/model_comparison_features_vs_esm2.py
+python scripts/train_baseline_classical_model.py
+Evaluates logistic regression and random forest baselines on the same feature set under the same LPOCV framework.
+
+Model Architecture
+ComponentDetailsInput features173 (128 PCA-reduced embedding dimensions + 45 biophysical)ArchitectureMLP: Input → 256 → 128 → 1 (sigmoid)ActivationReLU + Dropout (0.3, 0.2)OptimizerAdam (lr = 0.001, weight decay = 1×10⁻⁴)LossBinary cross-entropyEpochs30–50ValidationLeave-protein-out cross-validationPreprocessingStandardScaler + PCA fitted on training fold only
+Baseline comparison
+ModelAUC–ROCLogistic Regression + ESM-20.864Random Forest + ESM-20.882MLP + ESM-2 (proposed)0.899
+
+Dataset
+PropertyValueTotal variants11,928Pathogenic / likely pathogenic6,882 (57.7%)Benign / likely benign5,046 (42.3%)Proteins639SourceClinVar release 2024/12Protein sequencesUniProt release 2024_05Mitochondrial annotationMitoCarta 3.0 + OMIM
+The frozen dataset (data/frozen/) is MD5-checksummed for reproducibility.
+
+Benchmarking
+ToolN variantsAUC–ROCAUC–PROur model (MLP + ESM-2)11,7630.8990.923AlphaMissense11,2320.9420.954PolyPhen-27,3040.8450.799SIFT6,9970.8260.749
+AlphaMissense scores retrieved from the public bulk file (AlphaMissense_hg38.tsv.gz). PolyPhen-2 and SIFT scores retrieved via the UniProt Proteins variation API.
+
+Data Availability
+Raw data sources:
+
+ClinVar: https://www.ncbi.nlm.nih.gov/clinvar/
+UniProt: https://www.uniprot.org/
+MitoCarta 3.0: https://www.broadinstitute.org/mitocarta
+DisProt: https://disprot.org/
+MobiDB: https://mobidb.org/
+AlphaMissense scores: https://storage.googleapis.com/dm_alphamissense/AlphaMissense_hg38.tsv.gz
+
+Processed datasets and model checkpoints are available upon reasonable request to the corresponding author.
+
+Citation
+Harrizi S., Nait Idraha I., Mostafa K., Arnoult D. (2025).
+Mechanistically informed machine learning for pathogenicity prediction
+of mitochondrial missense variants. Bioinformatics (submitted).
+
+Authors
+Saad Harrizi — Laboratoire Santé, Environnement et Biotechnologie, Faculté Des Sciences Ain Chock, Université Hassan II de Casablanca, Maroc.
+Imane Nait Idraha — Laboratoire Santé, Environnement et Biotechnologie, Université Hassan II de Casablanca, Maroc.
+Kabine Mostafa — Laboratoire Santé, Environnement et Biotechnologie, Université Hassan II de Casablanca, Maroc.
+Damien Arnoult (Corresponding author) — INSERM UMR-S-MD 1193, Université Paris Saclay, Villejuif, France; INSERM-U1124, Université Paris Cité, Paris, France; CNRS Research Director (DR2).
+✉ damien.arnoult@inserm.fr
+
+License
+This repository is released for academic and research use. Please contact the corresponding author for commercial use inquiries.