biomedical-data

🧫 A curated list of resources relevant to doing Biomedical Information Extraction (including BioNLP)

An R package with over 50 highly cited, read-to-use, up-to-date COVID-19 pandemic data resources

MCP server for Open Targets data

BERT finetuned on NER downstream tasks

Measuring and visualizing biomedical data variability/heterogeneity across data sources

Bioinformatics Classifier Project — TCGA BRCA Dataset. Exploratory analysis and machine learning classification on TCGA BRCA gene expression data, focusing on PAM50 breast cancer subtypes.

Synthetic biomedical data generator for reproducible benchmarking of feature selection methods in high-dimensional machine learning.

Pinakes — a deterministic, verifiable data layer for AI agents across domains (biology + literature). Same query → byte-identical records, complete-or-fail-loudly, re-runnable provenance manifest + verify().

Набор инструментов для обработки радиобиологических Excel‑данных: визуализация опухолевого роста и кожных реакций, статистика, интерактивный GUI (PyQt6). Поддерживается оценка параметров LQ‑модели (α/β) и сравнение экспериментов.

Use ChatGPT, Claude, or Codex to search public dbGaP, UK Biobank, and All of Us metadata through an open-source MCP.

Healthcare AI project analyzing migraine treatment outcomes using longitudinal statistical models in R.

Three different basic data analysis processes of biomedical data for Python. Level: beginner (~200 lines of pure code).

R Shiny workspace integrating connected biomedical evidence for molecular target investigation.

Machine learning system for early Parkinson’s disease prediction using multimodal biomedical data (voice and handwriting) with Random Forest and EfficientNet models.

Step1-Step6 preprocessing workflow and final FAERS compound-PT-SOC core graph releases with standardized compounds, MedDRA PT/SOC mapping, and three pruned graph versions.

Machine learning for Raman spectra analysis of brain tissue with robust preprocessing, classification, and interpretable biomarker discovery

Multiclass classification of breast cancer subtypes using gene expression profiles. Evaluated and compared multiple models (Logistic Regression, Random Forest, HistGradientBoosting) using classification metrics, confusion matrices, and ROC-AUC analysis with Youden’s J statistic on synthetically generated data

ECG signal processing project covering preprocessing, noise filtering, peak detection, feature extraction and visualization using Python.

Automated harmonization of public omics metadata from GEO, SRA, BioSample, and tabular exports.

Explainable machine-learning project for malignant vs benign breast cancer classification using the Wisconsin Diagnostic dataset, cross-validation, and SHAP.