🧫 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

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.

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.

A lightweight Flask application for CSV upload, tabular preview, and basic data visualisation using Pandas and Matplotlib. Final project for CS50x.

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.

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

personal use for data of linked list