Manikandan Narayanan

ProcessNets: Towards an efficient approach for ensemble analysis of biological networks

ProcessNets efficiently analyzes ensembles of biological networks by leveraging their similarities, achieving significant space and time savings in network property computations.

Corpus-wide causality: Algorithm design & application for aggregating gene-disease causal evidence

This paper introduces a method to compute a Corpus-Wide Causal Score for gene-disease pairs by integrating network-based and literature-based evidence, outperforming LLMs in causal classification.

Systematic assessment of the biological impact of cellular deconvolution on downstream analyses of disease transcriptomes

This study assesses how cellular deconvolution affects transcriptomic analyses of Alzheimer's disease, showing that correction reshapes differential gene expression and pathway enrichment, revealing more consistent cell intrinsic signatures.

NLCD: A method to discover nonlinear causal relations among genes

NLCD detects nonlinear causal relationships between genes using nonlinear regression and conditional feature importance, outperforming existing methods on nonlinear datasets.

Uncertainty-Aware Gene Rankings Reveal Key Players in Coexpression Networks

The paper introduces uncertainty-aware gene ranking methods to improve stability in coexpression network analysis, showing better recovery of reference and tissue-specific genes compared to traditional rankings.

TB-Bench: A Systematic Benchmark of Machine Learning and Deep Learning Methods for Second-Line TB Drug Resistance Prediction

TB-Bench benchmarks ML and DL models for predicting second-line TB drug resistance using WGS data, finding traditional ML models outperform DL models in internal testing but show comparable performance in external validation.

Modeling and dissecting bidirectional feedback in gene-metabolite systems using the CausalFlux method

This paper introduces CausalFlux, a method modeling bidirectional feedback between genes and metabolites, showing improved prediction of reaction fluxes and gene essentiality compared to one-way feedback models.

An Atlas of Indian Genetic Diversity

The paper presents a comprehensive genomic atlas of Indian populations, identifying novel variants and highlighting genetic diversity and pharmacogenomic implications.

Dissecting the effect of single- and co- infection of TB and COVID-19 pathogens on the sputum microbiome

This study investigates how TB and COVID-19 co-infection affects the sputum microbiome, finding altered bacterial abundance and functional pathways that may impact treatment outcomes.

GViNC: an innovative framework for genome graph comparison reveals hidden patterns in the genetic diversity of human populations

GViNC compares genome graphs to reveal ancestry-specific genetic diversity patterns and key variant regions in human populations.

Gene Symphony: Two-Layered Musification of Gene Expression Clusters with Functional Backgrounds

This paper introduces a two-layered musification approach to represent gene expression clusters with functional context, showing it effectively conveys temporal and biological patterns through auditory representation.

Demixer: A probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample

Demixer identifies strain proportions and identities in mixed infection samples using a probabilistic generative model, achieving high accuracy in both synthetic and real-world datasets.

MultiPopPred: A Trans-Ethnic Disease Risk Prediction Method, and its Application to the South Asian Population

MultiPopPred improves polygenic risk score prediction in South Asians by 38% compared to existing methods, using a trans-ethnic approach with individual-level data.

Beyond associations: A benchmark Causal Relation Extraction Dataset (CRED) of disease-causing genes, its comparative evaluation, interpretation and application

The paper introduces a benchmark dataset (CRED) for causal relation extraction from biomedical literature, evaluates classifiers, and applies it to identify Parkinson's disease-causing genes.

Alzheimer's disease rewires gene coexpression networks coupling different brain regions

This study identifies gene coexpression rewiring between brain regions in Alzheimer's disease using differential correlation analysis of RNA-seq data from multiple brain regions.

Delineating markers of disease-disease interaction: a systematic methodology and its application to multiple diabetes-helminth cohorts

This study introduces a methodology to identify disease-disease interaction markers using comorbid diabetes and helminth infection cohorts, finding cytokines like IFN-γ and TNF- show significant interaction effects before treatment.

Robust discovery of causal gene networks via measurement error estimation and correction

This study introduces RCD, a method to improve causal gene network discovery by correcting measurement errors, showing better performance on simulated and yeast data and identifying causal relations in human skeletal muscle.

MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication

MultiCens identifies inter-tissue molecular mediators by analyzing multi-layer network centrality, revealing genes involved in hormone signaling and Alzheimer's disease brain regions.

Predicting cross-tissue hormone-gene relations usingbalanced word embeddings

The paper predicts hormone-gene associations across tissues using balanced word embeddings and a new dataset, identifying both known and novel genes involved in inter-tissue signaling.