Gaurav Ahuja

The paper introduces kGain, a metric quantifying k-mer frequency shifts due to mutations, and shows it predicts adaptive potential across species and experimental systems.

scKNIFE integrates gene expression with functional annotations to identify tumor programs and treatment-related functional variation in breast cancer.

NucXplore converts H&E-stained nuclei into 129 explicit features, outperforming classical and deep learning methods in histopathology cohorts and revealing aging and exposure-related cellular changes.

The paper introduces BioChirp, a system for reliable retrieval of structured biomedical data, outperforming existing agentic NL2SQL systems in accuracy and consistency.

MutAIverse detects known and novel DNA adducts and their precursor genotoxins using an AI-driven platform with mechanistic modeling and spectral mapping.

SMARTIE improves the analysis of RNA editing data to identify and rank RBP target RNAs by integrating statistical tests with confidence-weighted features.

The paper identifies endogenous sterols as intracellular allosteric modulators of the GPCR-Gα interface using a deep learning toolkit and experimental validation.

The paper introduces CDI, a framework that unifies multiple molecular representations into a single embedding, achieving better predictive performance than individual methods.

This paper introduces FORGE, a framework that jointly models drug response and gene essentiality to identify treatment groups and predict efficacy using basal gene expression.

SynGlue is an AI framework that designs multi-target therapeutics by combining data-driven and structure-guided methods, producing potent degraders for BRD4 and GSPT1.

RCoxNet improves cancer survival prediction by integrating mutation and clinical data with a deep learning Cox model and random walk algorithm, achieving better performance than existing models.

The paper introduces unCTC, an R package for unbiased identification and characterization of CTCs from scRNA-Seq data, validated on breast CTCs.

SCellBOW identifies and risk-stratifies tumor cell subpopulations using scRNA-seq data by simulating their impact on disease prognosis.

Stardust is a visualization method that embeds cells and marker genes together on a 2D map, performing competitively with existing tools on scRNA-seq and spatial transcriptomics data.

The paper develops Metabokiller, an AI model that predicts carcinogenic human metabolites by analyzing their biochemical properties and validates predictions through experimental assays.

The paper constructs a visual atlas of gene-pathology relationships by mining PubMed abstracts and using NLP to identify tissue-specific gene roles in disease.

The paper shows that RBPMS controls mRNA translation in pluripotent hESCs to preset cardiac fate competence, which is critical for cardiogenesis.

ROSeq provides a robust differential expression analysis method for scRNA-seq data by modeling expression ranks, showing improved noise tolerance and scalability.