Jishnu Das

FOCAL couples interpretable machine learning with gene regulatory networks to identify regulatory subnetworks underlying cell fate decisions in B and T cells.

This study examines how SIV infection affects spatial cellular organization and communication in pulmonary granulomas during SIV/Mtb co-infection, showing disrupted patterns not fully restored by ART.

FlowMap jointly models cellular states and dynamics in a geometric framework, recovering interpretable trajectories and developmental patterns from single-cell RNA sequencing data.

This study identifies a core gene program underlying Tfh cell differentiation using a multi-scale network systems approach, revealing a novel role for IL-12 in regulating Tfh precursor differentiation.

This study shows that T cell-specific loss of IRF1 impairs CD8 T cell activation and antitumor immunity, despite normal cytotoxic T cell numbers.

The paper identifies common hypomorphic TNFAIP3 variants associated with immune dysregulation and underdiagnosed haploinsufficiency diseases, suggesting widespread underrecognition of these conditions.

The paper identifies high cRel-expressing germinal center light zone B cells as enriched for precursor plasma cells, revealing cRel's role in promoting plasma cell generation.

This study shows Blimp1 restricts clonal dominance in germinal center B cells by controlling chromatin accessibility and BCR signaling during the light-to-dark zone transition.

This study identifies immune-driven injury pathways in the cochlea during chronic suppurative otitis media using proteomic profiling in a murine model.

SpaceTravLR identifies spatial functional microniches by modeling genetic perturbations across spatially resolved molecular networks, validated experimentally in a murine model.

This study shows that m6A RNA modification suppresses antifungal immunity in oral epithelial cells and that inhibiting the m6A pathway enhances antimicrobial peptide expression, reducing fungal burden in candidiasis.

SWING is a novel interaction language model that predicts peptide:MHC interactions and protein-protein interaction disruptions using sequence data.

The paper expands the DNA motif lexicon of transcriptional regulatory code by identifying composite elements and using a deep learning model to predict their activity and impact of variants.

The paper identifies loss of transitional B cell tolerance and extrafollicular expansion of autoreactive B cells as a common mechanism driving autoimmunity in monogenic CVID.

The paper predicts 3D structures of viral-host protein interactions and identifies evolutionary patterns and functional mechanisms underlying viral pathogenesis.

The paper analyzes co-authorship networks using interpretable machine learning to identify research trends and patterns in scientific collaboration over time.

This study shows that zinc availability in the tumor microenvironment affects anti-PD1 therapy response in CDKN2A-low tumors through macrophage phagocytosis.

The paper identifies transcription factor networks driving fibrotic lung disease using multiomic single-cell data from SSc-ILD and control lungs.

The paper identifies lineage-specific mutability patterns in SARS-CoV-2 spike protein to predict future mutations and guide vaccine/therapeutic design.

This paper introduces SCORPION, a tool for reconstructing gene regulatory networks from single-cell RNA-seq data, showing it outperforms existing methods and detects consistent regulatory differences in colorectal cancer.

SLIDE identifies significant interacting latent factors in multi-omic datasets, revealing biological insights in systemic sclerosis, spatial transcriptomics, and T1D models.

Essential Regression infers causal latent factors from multi-omic human datasets, outperforming existing methods in prediction performance and enabling causal inferences.

The paper uses proteomics to identify ECM injury in kidney allografts with early AMR, finding decreased ECM proteins and increased LGALS1 and GSTO1 in affected tissues.