Name: Christopher L Plaisier, PhD
Type: User
Company: Arizona State University
Bio: Assistant Professor, School of Biological and Health Systems Engineering
Arizona State University
Temp, Arizona
Location: Tempe, Arizona
Blog: https://plaisierlab.engineering.asu.edu
Christopher L Plaisier, PhD's Projects
Java library to simulate Boolean Dynamic models
Boolean Network Modeling
Builds a Transcription Factor Binding Site (TFBS) database (DB) for use as an input to cMonkey via set enrichment.
A database containing the predictions for the Cancer miRNA-Regulatory Network.
Java project related to the paper "Cell fate reprogramming by control of intracellular network dynamics" (http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004193)
cMonkey integrated biclustering algorithm
Python port of cMonkey, a machine-learning based method for clustering
Copy number variant detection from targeted DNA sequencing
A JavaScript visualization library for HTML and SVG.
Python/C implementation of Hartigan & Hartigan's dip test, based on Martin Maechler's R package
Framework for Inference of Regulation by miRNAs (FIRM)
Website backing publication.
Hopfield Networks for visualizing the state space of different samples.
Inferring CNV from Single-Cell RNA-Seq
R scripts for introduction to systems biology at ISB.
Java library that implements the Jonhson Cycle algorithm and adapts it to the search for stable motifs.
An interface to get the latest KEGG data and compare it to sets of genes for over-representation.
LiberMate - A MATLAB to Python (SciPy/NumPy) Translator
Identify miRNA that binds to a PSSM motif from 3' UTR.
Stand alone version of miRvestigator HMM that takes as input a FASTA stlyed PSSM file.
A web server version of the miRvestigator framework.
Code for normalizing count data.
Code to access cMonkey RData objects and provide the data in a Python friendly manner.
A suite of tools to post process gene regulatory networks.
Software to integrate somatic protein affecting mutations (PAMs) and copy number alterations (CNAs) for downstream computational analyses.
Binarizes gene expression data using a k-means based approach that is ported from the R package Binarize.
EGA (European Genome-phenome archive) downloader
Single cell analysis tools for expression from RNA-seq in R
Extracts sequence for promoter and 3' UTR regions. Eventually will be developed to extract all potential cis-regulatory regions.