Palakkad Krishnan Unni Vinod

The paper identifies conserved gene signatures predictive of allograft rejection across multiple organs using transcriptomic data and machine learning.

This study analyzes spatial transcriptomics data from ovarian cancer patients post-chemotherapy to identify spatially distinct pathway activities and their association with treatment response.

DeepGraphMut uses graph-based deep learning to predict cancer prognosis from somatic mutation data, identifying subtypes and achieving high survival prediction accuracy across 16 cancer types.

This study identifies hypometabolism in ASD by analyzing transcriptomic data from brain and blood samples, revealing dysregulated metabolic pathways in both tissues.

This study uses a single-cell network approach to analyze metabolic regulation in gynecologic and breast cancers, identifying key transcriptional regulators and metabolic pathways involved in tumor heterogeneity.

This study uses meta-analysis of Leishmania donovani transcriptome data to identify co-regulated gene modules and reveals reciprocal regulation between two modules associated with different life phases.

This study identifies two metabolic subtypes of endometrial cancer using transcriptomics and metabolic modeling, linking them to clinical outcomes and genomic alterations.

This study analyzes liver regeneration using temporal RNA sequencing and mathematical modeling to reveal regulatory networks and bistable behavior in HNF4A.

This study identifies metabolic changes in SARS-CoV-2 infection using transcriptomic data from cell lines and patient samples, revealing both pro- and antiviral metabolic alterations.

This study quantifies ASD severity by analyzing dynamic functional connectivity variability and modular organization in resting-state fMRI data across different age groups.

This study investigates functional connectivity differences in ASD versus TD using graph-theoretic analysis across two datasets, finding age-related and disease-modulated changes in network properties.