Vaibhav Vindal

The paper identifies vulnerable proteins in HPV-positive and HPV-negative HNSCC using PPI networks and multi-omics data, and predicts synthetic lethal partners with potential therapeutic targets.

The paper identifies shared driver genes across breast tissue transitions from normal to tumor states and TANTs to tumor subtypes, revealing their involvement in cancer-related pathways.

The paper constructs and analyzes PPI networks of breast tumor and adjacent normal tissues to identify vulnerable proteins and influential nodes associated with breast cancer.

This study identifies ALOX12B as a driver of skin inflammation through the inflammasome/Th17 signaling axis, showing its overexpression in psoriasis and atopic dermatitis and demonstrating a novel inhibitor's efficacy in a mouse model.

The paper integrates DNA methylation and non-coding RNA data from breast tumor and adjacent normal tissues to identify key regulatory hubs and bottlenecks involved in transcriptional misregulation in cancer.

LCLNCRdb is a manually curated database of 1102 lung cancer-associated lncRNAs with expression, network, and survival data, providing a resource for research and data sharing.

This study prioritizes lung cancer candidate genes using moment of inertia tensor analysis of amino acid physicochemical properties, identifying four genes linked to poor survival.

NetVA identifies key molecules in disease-specific PPI networks using network vulnerability and EVC+ analysis, highlighting essential proteins and hubs in breast cancer.

The paper constructs and analyzes gene regulatory networks for breast cancer subtypes, adjacent normal tissues, and normal tissues to identify subtype-specific and pre-cancerous regulatory features and candidate genes.

BCLncRDB is a database of breast cancer-associated lncRNAs containing 5,279 associations with features like expression, methylation, and drug information.