YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

Rapids_singlecell: A GPU-accelerated tool for scRNA analysis. Offers seamless scverse compatibility for efficient single-cell data processing and analysis.

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QDMR (Quantitative Differentially Methylated Regions) is a quantitative approach to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy.

Shiny for Python

🦅Hyperparameter optimization for machine learning pipelines 🦅

Modular framework for deep learning predictions on methylation data.

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A modern LaTeX Beamer theme

DeepMP is a computational tool to detect DNA modifications in Nanopore sequencing data

R toolkit for the analysis of single-cell chromatin data

WGBS/NOMe-seq Data Processing & Differential Methylation Analysis

cisTopic: Probabilistic modelling of cis-regulatory topics from single cell epigenomics data

Explore design matrices interactively with R/Shiny

Single-cell epigenomics analysis tools

An accurate and efficient deep learning method for single-cell RNA-seq data imputation

COMETgazer mehylation analysis software suite

This package contains a reference-based function to infer the proportions of a priori known cell subtypes present in a sample representing a mixture of such cell-types. Inference proceeds via one o…

chromatin Variability Across Regions (of the genome!)

A Brief Summary Of Best Practices For Bioinformatics

Using Deep Learning to predict gene annotations

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A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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Mathematical derivation and pure Python code implementation of machine learning algorithms.