Svetha Venkatesh

This paper introduces a neural network model called TATA to learn distance-dependent motif interactions in RNA splicing using self-attention and Toeplitz max pooling, and applies it to identify cis-regulatory elements interacting with splice sites.

This paper introduces PAN, a method to create sample-specific gene networks using annotations, improving breast cancer relapse prediction compared to existing methods.

The paper predicts gene-drug responses for individual patients by integrating single-cell expression data with drug-response databases using a dual-channel random walk method.

Representing drugs as molecular graphs, GraphDTA lowers drug-target affinity prediction error to MSE 0.229 on Davis and 0.139 on Kiba versus deep learning baselines.

The paper introduces HyperXPair, an interpretable neural model that learns distance-dependent motif interactions for transcription initiation and RNA splicing.

The paper introduces a sparse neural encoder-decoder network to predict gut metabolites from microbiome data, showing improved accuracy and interpretability over traditional methods.

This paper introduces an anomaly detection approach to classify cancer using only healthy tissue transcriptomes, showing improved accuracy with more normal samples.

This paper improves neuropsychiatric condition classification by using gene ontology terms as features, enhancing performance, stability, and interpretability of classifiers.

This study identifies sex-dependent and -independent biomarkers in ASD using post-mortem brain RNA-Seq and blood microarray data, highlighting sex-specific gene expression differences and pathway dysregulation.