24 citations · 30 across the 4 of their papers we have counts for
4 papers
Revisiting invariances and introducing priors in Gromov-Wasserstein distances
Pinar Demetci, Quang Huy Tran, Ievgen Redko +1
Gromov-Wasserstein distance has found many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformati…
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Ritambhara Singh, Jack Lanchantin, Gabriel Robins +1
Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related metho…
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Jack Lanchantin, Ritambhara Singh, Beilun Wang +1
Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remai…
DeepChrome: Deep-learning for predicting gene expression from histone modifications
Ritambhara Singh, Jack Lanchantin, Gabriel Robins +1
Motivation: Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification si…