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Machine learning framework to quantify pathogenicity of structural variants
ThermoNet is a computational framework for quantitative prediction of the impact of single-point mutations on protein thermodynamic stability. The core algorithm of ThermoNet is an ensemble of deep 3D convolutional neural networks.
Bayesian model to integrate two types of ChIP-seq controls for binding site detection
Solution to iDASH'19 challenge track 1
Optimizing Cancer Mutation Signatures Jointly with Sampling Likelihood
Predicting privacy risk of functional genomics data
STARRPeaker: STARR-seq peak caller
Code to reproduce the analyses described in the manuscript, "Approaches for integrating heterogeneous RNA-seq data reveals cross-talk between microbes and genes in asthmatic patients"
a method to identify TADs in Hi-C data
ESPRNN: Epigenome-based Splicing Prediction using Recurrent Neural Network
ALOFT, the Annotation Of Loss-of-Function Transcripts, provides extensive functional annotations to loss-of-function variants in the human genome.
Local Event-based analysis of alternative Splicing using RNA-Seq
TeXP is a pipeline to gauge the autonomous transcription level of L1 subfamilies using short read RNA-seq data
Modelling tumor progression from a single VCF file
GRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell type-specific manner
Spectral and reproducibility analysis of Hi-C contact maps
CBB752 SP2019 Course Website
MUltiScale enrIchment Calling for ChIP-Seq Datasets