59 citations · 60 across the 3 of their papers we have counts for
5 papers
DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications
Arshdeep Sekhon, Ritambhara Singh, Yanjun Qi
Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the funct…
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
Jack Lanchantin, Arshdeep Sekhon, Ritambhara Singh +1
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TF…
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon +1
The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation…
Memory Matching Networks for Genomic Sequence Classification
Jack Lanchantin, Ritambhara Singh, Yanjun Qi
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manual…
Deep Motif: Visualizing Genomic Sequence Classifications
Jack Lanchantin, Ritambhara Singh, Zeming Lin +1
This paper applies a deep convolutional/highway MLP framework to classify genomic sequences on the transcription factor binding site task. To make the model understandable, we prop…