Suhas Srinivasan

The paper identifies estrogen-modulated SNVs associated with breast cancer risk and shows their interaction with reproductive factors using polygenic risk scores.

This paper identifies Raf1 within mitochondria where it interacts with GLS to regulate glutamine catabolism and tumorigenesis, independent of MAPK signaling.

This paper develops an agent-based model to simulate and predict infectious outbreaks in confined environments, showing it can accurately predict infection numbers and suggest improved containment protocols.

PLI-Analyzer evaluates AI-predicted biomolecular interfaces by analyzing atomic contacts and domain annotations, revealing discrepancies in predicted protein-protein complexes.

The paper identifies disease-linked regulatory DNA variants and their impact on epidermal homeostasis by analyzing SNVs, TF binding, and gene dysregulation in polygenic skin diseases.

This study identifies glucose-binding proteins TSC22D4 and IGF2BP2 using thermal proteome profiling, showing their roles in metabolic disease and insulin secretion.

The paper shows that ecDNA-borne PVT1 fusions stabilize oncogenic mRNAs by interacting with SRSF1, enhancing RNA stability and supporting MYC dependency in cancer.

The paper introduces irCLIP-RNP and Re-CLIP methods to identify dynamic RNA-protein assemblies, revealing new patterns of RBP-RNA associations and EGF-induced remodeling.

The paper identifies autoantibody hotspots on the XIST RNP complex in female-biased autoimmune diseases and links them to clinical outcomes.

This study shows that Xist ribonucleoprotein complexes drive sex-biased autoimmunity in males, leading to autoimmune pathology and reprogramming of immune cells to resemble female patterns.

The paper analyzes population-specific mutations' impact on the human interactome using in-silico edgetic profiling of nsSNVs from the 1,000 Genomes Project.

This paper introduces a hybrid deep learning method for cell type clustering in scRNA-seq data, achieving performance comparable to supervised learning.

The paper uses computational methods to analyze structural genomics and interactomics of 2019-nCoV, comparing it to SARS coronavirus to identify conserved functional regions.

The paper introduces DIMSUM, a computational framework that integrates GWAS, mutation effects, and PPI networks to detect disease-specific modules in the human interactome, showing improved biological relevance and disease association.