Venkatraman Seshan

Integrated Clinicogenomic Risk Modeling for Metachronous Second Primary Cancers

The paper develops and validates a clinicogenomic risk model to predict second primary cancers in cancer survivors using genetic, clinical, and lifestyle factors.

Improving Accuracy of Somatic Mutation Profiling in Large Epidemiologic Studies: Addressing Cases without Matched Normal Samples

The paper improves somatic mutation detection in tumor samples without matched normal samples by estimating tumor purity and variant allele frequency, reducing false positive and negative rates.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks

CellGate automates spatial immunophenotyping of multiplexed tissue images with high accuracy, enabling scalable analysis of tumor-immune microenvironments.

Enhanced clinical assessment of hematologic malignancies through routine paired tumor:normal sequencing

This study presents a paired tumor:normal sequencing dataset for hematologic malignancies, showing improved identification of somatic variants and their clinical relevance.

Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering

This paper introduces survClust, an outcome-weighted clustering algorithm that identifies clinically relevant genomic subtypes in pan-cancer data, outperforming unsupervised methods in detecting subpopulations with aggressive clinical behavior.