Vakul Mohanty

This paper introduces TCR-FramePose, a geometric framework for analyzing TCR-pMHC recognition, showing it captures novel structural features beyond conventional descriptors.

This paper introduces ABMACT, an agent-based model to simulate tumor-immune dynamics in adoptive cell therapy and identifies key factors for optimal efficacy.

The paper constructs an integrated atlas of tumor-infiltrating dendritic cells across species and immunotherapy conditions, revealing conserved activation states and spatial reorganization in response to therapy.

This study shows that romaciclib combined with venetoclax overcomes venetoclax resistance in AML through synergistic apoptosis induction and suppression of resistance pathways.

The paper constructs an immune cell knowledge graph (ICKG) using AI to annotate immunological gene sets more accurately than existing methods.

The paper constructs immune-specific gene sets using non-negative matrix factorization on bulk RNA-seq data to improve immune subtype analysis and ICB response prediction.

This study identifies four transcriptionally distinct venetoclax-resistant clusters in AML, revealing phenotypic, genetic, and drug response differences and their implications for treatment resistance.

novoRNABreak detects novel splice junctions and fusion transcripts in cancer RNA-seq data with high sensitivity and efficiency.

The paper develops METAFlux to infer metabolic fluxes from bulk and single-cell RNA-seq data, showing improved accuracy over existing methods in cancer TME analysis.

The paper models scRNA-seq data from developmental processes using SDEs to identify transition cells that explain heterogeneity and predict cell fate in early tumorigenesis.

Sensei determines required sample sizes for detecting cell type abundance changes in single-cell studies using a beta-binomial model and t-test expansion.

SCMER identifies non-redundant molecular features that delineate both common cell lineages and rare cellular states in single-cell data.

The paper introduces MEDALT and LSA to reconstruct copy number lineage trees from single-cell data and identify fitness-associated genes, showing improved accuracy over phylogenetics methods in cancer datasets.

bindSC integrates single-cell multi-omics data accurately by aligning rows and columns without approximations, outperforming existing methods in various biological contexts.

OmicPioneer-sc provides an interactive visualization tool for single-cell sequencing data, integrating dimensionality reduction, heat maps, and pathway modules to aid in cell-type annotation and multi-omic analysis.