Shreya Paithankar

GEOMeta automates metadata curation for GEO transcriptomic data, generating standardized annotations for 600,000 human bulk RNA-seq samples and benchmarking LLMs for annotation quality.

The paper uses single-cell transcriptomics to identify S100 gene expression patterns and cell-type changes in MODS patients, showing diagnostic potential and therapeutic implications.

The paper develops a computational pipeline to identify bispecific antibody targets by integrating bulk, single-cell, and spatial transcriptomics data, finding GPC3 and MUC13 as promising candidates in hepatocellular carcinoma.

The paper develops a tool to detect viral infections in cell lines using RNA-seq data and machine learning, finding 10% of MCF7 experiments were likely infected.

The paper identifies dihydroxyeicosadienoic acid as a metabolite of DGLA that induces ferroptosis-mediated neurodegeneration in dopaminergic neurons.

TransCell predicts genomic and cellular responses from gene expression using transfer learning, achieving strong performance in drug sensitivity and gene effect score prediction.

The paper identifies common mechanisms of action among anti-SARS-CoV-2 drugs and shows that targeting cholesterol homeostasis and microtubule cytoskeleton organization synergizes with antivirals.

This study evaluates the genomic similarity between breast cancer cell lines and metastatic samples, finding significant differences and highlighting organoids as better models.