Materials on the analysis of microarray expression data; focus on re-analysis of public data ( http://tinyurl.com/cruk-microarray)

Deep learning methods for feature selection in gene expression autism data.

Bioinformatics course project - Fall 2020, analysis of genetic expression omnibus (GEO) data series of Acute Myeloid Leukemia

Tumor prediction from microarray data using 10 machine learning classifiers. Feature extraction from microarray data using various feature extraction algorithms.

Collection of gene expression datasets

A visualization support tool for advanced hierarchical clustering analysis. MLCut allows cutting dendrograms at multiple heights/levels. In other words, it allows to set multiple local similarity thresholds in potentially large dendrograms. It uses two coordinated views, one for the dentrogram (radial layout), and another for the original multid…

Discriminant Analysis with Network PROcessing

A python library to develop genomic data simulators

An Efficeint and Fast Wrapper-based High-dimensional Feature Selection(SIFE) in MATLAB

NOWAC data cleaning package

Generating expression signatures for disease using STARGEO

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

This R script is used to analyze microarray data acquired by an Agilent SureScan Microarray Scanner.

Scripts using GEOquery package to fetch expression matrices and phenotypic data associated with GSE datasets

protGear is a package for protein microarray data processing just before the main analysis.

Code for the future "Classification of microarray data" paper.

Various python programs I use for the manipulation of SNP genotype datasets and other bioinformatics tasks. This repository features miscellaneous scripts to help work efficiently in common bioinformatics tasks I encounter. Use at own risk

A structural equation modeling approach for the identification of gene expression adaptation in breast cancer patients at grade specific level

EXIS is an interactive R shiny application for isoform-specific expression analysis.

This is a final group project for Computational Biology (CS167) implementing Tempo (http://bcb.cs.tufts.edu/tempo/) on a unique Parkinson's dataset. This project was contributed to equally by Kevin Kapner, Carlos Lopez-Rodriguez, and Qing Zhu.