A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
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Updated
Apr 5, 2024 - Jupyter Notebook
A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
Code for the paper "Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts"
Detecting Regulatory Elements using GRO-seq and PRO-seq
Fast Inference of Networks from Directed Regulations
Single-cell RNA-seq data-based inference of multilayer inter- and intra-cellular signaling networks
pyJASPAR: A Pythonic interface to JASPAR transcription factor motifs
An R package for multi-dimensional pathway enrichment analysis
A “data light” TF-network mapping algorithm using only gene expression and genome sequence data.
Code and data used to create the JASPAR UCSC Genome Browser tracks data hub
Code and data used by the JASPAR profile inference tool
Analysis of regulatory impacts of autism-associated SNPs on biological pathways in the fetal and adult cortex.
Crosstalk between codon optimality and 3' UTR cis-elements dictates mRNA stability
A powerful abstraction of gene databases
Depicting pseudotime-lagged causality for accurate gene-regulatory inference
target: An R Package to Predict Combined Function of Transcription Factors
CREST-seq peak calling.
Meta-analysis of DNA methylation in ART
Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data
An integrative toolkit for detecting cell type-specific regulators
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