Training and evaluating a variational autoencoder for pan-cancer gene expression data

Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

GREIN : GEO RNA-seq Experiments Interactive Navigator

Haystack: Epigenetic Variability and Transcription Factor Motifs Analysis Pipeline

An R package to plot interactive three-way differential expression analysis

Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously

RAPToR (Real Age Prediction from Transcriptome staging on Reference) is a tool to accurately predict individual samples' developmental age from their gene expression profiles.

BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

Gene expression viewer template

Example workflows for refine.bio data

Jose V. Die

Multi-class KRAS/NRAS Classifier for Multiple Myeloma

Material, scripts and datasets for the MolBioMed Bioinformatics Class

Hippocampal transcriptomic responses to enzyme‐mediated cellular dissociation

Applying Machine Learning Ras, NF1, and TP53 Classifiers to PDX model gene expression

This readme repository contains sample codes for executing steps within our manuscript

Cell type-specific eQTL mapping

identifying and annotating uninvestigated disease-associated genes

Transform, query, and merge tabular files with the expressionable Python module. This tool is used primarily for gene-expression data.

Gene Network Inference from Expression Time Series