Generating expression signatures for disease using STARGEO
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Updated
Mar 29, 2016 - Jupyter Notebook
Generating expression signatures for disease using STARGEO
Analysis of microarray data from dHPC of rats treated with lps for 6h
Materials on the analysis of microarray expression data; focus on re-analysis of public data ( http://tinyurl.com/cruk-microarray)
pmadsim — Prospective Microarray Data Simulation. This is a fork of the madsim R package.
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
NOWAC data cleaning package
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.
PCTA web application by Django
Transform, query, and merge tabular files with the expressionable Python module. This tool is used primarily for gene-expression data.
Code for my master thesis: "Microarray data analysis in prediction of breast cancer metastasis"
Scripts using GEOquery package to fetch expression matrices and phenotypic data associated with GSE datasets
TriCluster and microCluster gene expression clustering algorithms
MicAff is a genomic data analysis tool. Application made with Shiny. It is used to analyze the data from the microarray experiment. Running on a local server, it allows you to load CEL data, their initial processing and further analysis.
Tumor prediction from microarray data using 10 machine learning classifiers. Feature extraction from microarray data using various feature extraction algorithms.
Differential Gene Analysis of Telomere Microarray Data
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…
Use Deep Learning Methods to analyze gene based microarray data to make classifcations on diseases, especially cancers, where the model is going to identify cancer stages for different cancers.
Bioinformatics course project - Fall 2020, analysis of genetic expression omnibus (GEO) data series of Acute Myeloid Leukemia
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