Giri Narasimhan

EMoMiS identifies molecular mimicry between SARS-CoV-2 proteins and viral/human proteins, offering insights into cross-protection and side effects.

PathPCNet predicts drug sensitivity using multi-omics data and biological pathways, achieving high correlation and uncovering key gene-drug interactions.

This study surveys and evaluates state-of-the-art scoring functions for protein-protein docking, comparing classical and deep learning-based approaches.

The paper introduces METALICA, a tool for causal inference in microbiome data, to uncover interactions and confounders in longitudinal multi-omic datasets.

The paper introduces a visualization tool for metagenomic data using Hilbert curves to create interactive 2D microbiome maps that highlight microbial abundance patterns.

Epitopedia identifies regions of molecular mimicry between pathogens and immune epitopes by analyzing structural similarity and sequence identity.

The paper introduces PIsToN, a transformer-based model for predicting protein binding interfaces, which outperforms existing methods on benchmark datasets.

The paper identifies molecular mimicry between SARS-CoV-2 Spike and human proteins that may contribute to autoimmune responses in COVID-19.

This paper introduces a multi-run Concrete Autoencoder (mrCAE) to identify prognostic lncRNAs across 12 cancers, achieving 95% accuracy in cancer classification and identifying 76 prognostic lncRNAs.

The paper introduces a computational pipeline, PALM, that integrates multi-omics time-series microbiome data using dynamic Bayesian networks to identify microbial and host interactions.

The paper compares PLS-DA and PCA for feature selection using synthetic and microbiome data, showing PCA can outperform PLS-DA in some cases.

This paper introduces a signed Bayesian Network (sBN) to infer directional relationships in microbial communities using abundance data, finding that directed edges correlate with colonization order.

The paper introduces a method to analyze microbiome antibiotic response using replication rates and causal inference, finding microbes reduce replication under beta-lactams and specific resistance genes enable dominance.

This paper applies causal inference techniques to microbiome data to study antibiotic resistance, introducing a novel pipeline for predicting interventional effects without controlled experiments.