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Solution to iDASH'19 challenge track 1
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.
Optimizing Cancer Mutation Signatures Jointly with Sampling Likelihood
Machine learning framework to quantify pathogenicity of structural variants
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