Conquering confounds and covariates: methods, library and guidance

A Python package for gene network analysis

missing data handing: visualize and impute

pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets

Bayesian Gene Heritability Analysis from GWAS summary statistics

An Intuitive App for Statistical Analysis

Stochastic Gene Ontology Enrichment Analyses (GOEA) Simulations in manscript + Multiple-Test Correction Simulations

This is the repository of BioMedStatX. A comprehensive tool for statistical analysis of experimental data. Users can import Excel or CSV files, select groups and let BioMedStatX manage the rest: from outlier detection, assumption checks and guided data transformations to test execution (parametric or non-parametric) and guided post-hoc analyses.

Heart Hybrid Classifier is a machine learning framework focused on cardiovascular risk prediction. The goal is to evaluate accuracy and interpretability of the models in a clinical setting.

Examples of code that can be used to perform data analysis in R or Python.

Interactive Streamlit app that runs over 15 causal discovery algorithms with method recommendation and bootstrap confidence graphs.

Open Source Web application for bioinformatics,biologist,data scientist

Command line tool that writes commands to run one by one to perform analyses (mainly using qiime2) on a HPC running Slurm, Torque or none of these.

Data Mining and Machine Learning at Big Data Summer Institute

📊 Upload CSV/Excel files to generate boxplots, run ANOVA, and auto-calculate Tukey HSD — no coding needed.

MPH Capstone Data Management & Analysis

An free and open source community implementation of Chai-1 in PyTorch

Optimization and Evolutionary Algorithms in Python: This repository provides Python implementations of popular optimization and evolutionary algorithms, designed to tackle complex computational problems inspired by natural processes. These algorithms are widely used in various domains like optimization, machine learning, and engineering.

BioMetaGenie - a wrapper toolkit

My internship at the IBEAS lab