Chakit Arora

This study analyzes mutations in neural signaling pathways across cancers, finding 65% of patients have alterations linked to poor survival and complex pathway interactions.

The paper analyzes cell-cell interactions (CCIs) in transcriptomic data, showing they are more co-expressed in cancer and associated with patient survival and immunotherapy response.

The paper develops interpretable models to predict cancer drug sensitivity using transcriptomics data and enhances predictions with large language models, validated on clinical samples.

The paper analyzes GPCR signaling dysregulation in cancer transcriptomics data to identify therapeutic targets and survival-associated signaling axes.

The paper introduces a software package that computes over 50,000 features for protein/peptide function prediction, including residue-level annotations.

The paper identifies nine apoptotic pathway genes associated with prognosis in papillary thyroid carcinoma and develops risk prediction models with high accuracy.

The paper identifies prognostic biomarkers for UCEC survival using gene expression profiles of PRRs and develops risk stratification models with improved performance when combined with clinical data.

The paper develops prediction models using HLA-alleles and clinical characteristics to predict overall survival in skin cutaneous melanoma patients and identifies favorable and unfavorable superalleles.

This study develops a hybrid model combining BLAST and machine learning to predict pattern recognition receptors, achieving high accuracy with an MCC of 0.82 and AUROC of 0.95.

This paper develops models to predict risk scores for colorectal cancer patients using protein concentrations in the mitochondrial apoptotic pathway, achieving higher accuracy than existing tools.

The paper develops a 5D ODE model to study p53 dynamics in metabolic stress-induced cellular senescence and identifies strategies to reduce inflammation and insulin resistance.