Deep neural networks implemented in TensorFlow & Python for predicting whether transcription factors will bind to given DNA sequences
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Updated
Dec 9, 2016 - Python
Deep neural networks implemented in TensorFlow & Python for predicting whether transcription factors will bind to given DNA sequences
MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets
Patterns of Binding Targets
Unofficial fork of the DeepSEA deep learning genomics project
Motif discovery for DNA sequences using multiobjective optimization and genetic programming.
BiasAway will improve TFBS enrichment analyses and the applied analysis of ChIP-Seq data, particularly for the annotation of reliable TFBSs within ChIP-Seq peaks.
Prediction of the binding sites of multiple transcription factors in a whole genome
Bioinformatics study on tfbs and their association with specific tissues.
An integrative toolkit for detecting cell type-specific regulators
A motif discovery tool to detect the occurrences of known motifs
Scans TF binding sites based on motifs.
Bioinformatic approach to identify functional transcription factor binding motifs
A repository with exploration into using transformers to predict DNA ↔ transcription factor binding
Dual threshold optimization for identifying convergent evidence: TF binding locations and TF perturbation responses.
Pipeline for predicting ChIP-seq peaks in novel cell types using chromatin accessibility
Data challenge with kernel methods - MVA MSc
Discover transcription factor (TF) binding specificities/sites (TFBS) using binding site motif sequence and structural information.
MYB transcription factors are one of the largest gene family in plants and control many processes. This repository provides additional background to the #MYB_Monday tweets
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