60 citations · 175 across the 14 of their papers we have counts for
14 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…
One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data
Michal Golovanevsky, Eva Schiller, Akira Nair +3
Multimodal learning models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to autonomous driving. Des…
Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis
Michal Golovanevsky, Carsten Eickhoff, Ritambhara Singh
Alzheimer's Disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support diffic…
Unbalanced CO-Optimal Transport
Quang Huy Tran, Hicham Janati, Nicolas Courty +4
Optimal transport (OT) compares probability distributions by computing a meaningful alignment between their samples. CO-optimal transport (COOT) takes this comparison further by in…
A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
Camillo Saueressig, Adam Berkley, Reshma Munbodh +1
We present a joint graph convolution-image convolution neural network as our submission to the Brain Tumor Segmentation (BraTS) 2021 challenge. We model each brain as a graph compo…
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…
GaKCo: a Fast GApped k-mer string Kernel using COunting
Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari +3
String Kernel (SK) techniques, especially those using gapped -mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, t…
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…
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…
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…
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
Beilun Wang, Ritambhara Singh, Yanjun Qi
Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulat…