🧫 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
BERT finetuned on NER downstream tasks
MCP server for Open Targets Data
Measuring and visualizing biomedical data variability/heterogeneity across data sources
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.
Three different basic data analysis processes of biomedical data for Python. Level: beginner (~200 lines of pure code).
Bioinformatics Classifier Project — TCGA BRCA Dataset. Exploratory analysis and machine learning classification on TCGA BRCA gene expression data, focusing on PAM50 breast cancer subtypes.
Project focused on exploring and modeling the T1DiabetesGranada dataset, which contains clinical, biochemical, and continuous glucose monitoring (CGM) data from patients with Type 1 Diabetes.
Healthcare AI project analyzing migraine treatment outcomes using longitudinal statistical models in R.
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
Multiclass classification of breast cancer subtypes using synthetic gene expression data. Refactored code to use a single function for model evaluation across Logistic Regression, Random Forest, and HistGradientBoosting, including metrics and ROC-AUC with Youden’s J statistic.
Machine learning system for early Parkinson’s disease prediction using multimodal biomedical data (voice and handwriting) with Random Forest and EfficientNet models.
A lightweight Flask application for CSV upload, tabular preview, and basic data visualisation using Pandas and Matplotlib. Final project for CS50x.
A lightweight R script for text mining and harmonizing medical phenotype data. Cleans, standardizes, and maps diagnoses to ICD-10 codes, with clinical annotations for enhanced data usability.
Official repository for the paper “Boosting OOD Detection in Biomedical Data with Siamese Neural Networks".
MCP server for MyDisease.info API
A MATLAB pipeline for classifying FourClass Motor Imagery EEG signals. Implements CSP/FBCSP feature extraction and SVM/CNN/LSTM models, achieving 98.75% accuracy with an optimized Linear SVM. Modular code for preprocessing, feature selection, and classification.
A predictive modeling pipeline that leverages machine learning to classify and forecast health conditions based on clinical indicators, emphasizing feature importance and model reliability.
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