Bioinformatics and Genomic Networks

This paper introduces SBM-GNN, a graph neural network model that identifies cancer driver genes and pathways from omic data, outperforming existing methods and revealing key cancer-related processes.

The paper identifies context-dependent functional interactions from CRISPR screens, revealing how genotype and lineage influence gene coessentiality and pathway rewiring.

The paper introduces vWCluster, a network-based clustering method for multi-omics breast cancer data using vector-valued optimal transport, identifying tumor subtypes with distinct survival rates and immune microenvironment profiles.

SLIMP predicts metabolite-protein interactions using supervised machine learning on co-fractionation mass spectrometry data, identifying over 9,000 and 20,000 interactions in yeast and Arabidopsis.

This study identifies MCM7 as a differentially expressed gene common to breast cancer and Alzheimer's disease, showing opposite expression patterns and suggesting potential therapeutic relevance for Alzheimer's.

This study identifies differentially expressed genes and hub genes in type 1 diabetes mellitus using bioinformatics analysis and validates their association with the disease.

NeTOIF improves time-series omics data imputation and forecasting by integrating network-based methods and LSTM networks, outperforming existing techniques in handling missing values across multiple time points.

This paper introduces a method using Protein Locality Graph topology to infer species trees by analyzing inter-module interactions in protein networks.

The paper updates ePlant to support 15 new plant species and adds new viewers and data sets, including single-cell RNA-seq data for Arabidopsis roots.

PINAWeb compares multiple protein-protein interaction network aligners to help researchers evaluate their performance and select the best tool for their needs.

The paper analyzes SARS-CoV-2 infection's targeting of indispensable nodes in a directed human protein-protein interaction network using proteomics and CRISPR data, showing differential controllability among coronaviruses.

This study identifies key genes and pathways associated with dementia using integrated bioinformatics analysis of a high-throughput sequencing dataset.

BioNetComp compares biological networks using metrics and visualization, demonstrating its application on interactomes from different stages of bioethanol fermentation.

DDN2.0 provides R and Python tools for differential dependency network analysis to identify key pathways and network rewiring across omics data.

The paper identifies three distinct protein types in human PPI networks—pure hubs, mix proteins, and pure bottlenecks—based on their topological properties and functional roles.

This study identifies 925 differentially expressed genes in T2DM using bioinformatics analysis and highlights 10 hub genes as potential biomarkers and therapeutic targets.

The paper compares rotation-based scores for gene set analysis and finds the average of absolute values is the most powerful score.

The paper identifies hub proteins in a disease-gene network model for chronic myeloid leukemia (CML) and suggests potential drug targets.

The paper constructs a high-quality yeast binary interactome map and compares inner- and outer-complexome organization, finding the outer-complexome contains more interactions and exhibits higher functional heterogeneity.

The paper analyzes matrisome gene expression patterns across tissues, age, sex, and disease using GTEx and TCGA data, finding that transcriptomics can recapitulate proteomic patterns and identify tissue-specific and sex/age-related expression variations.

GraphBreak detects and annotates communities in regulatory networks to prioritize genes for disease associations and drug targeting.

DeepMOCCA integrates multi-omics data with biological networks to predict cancer survival and identify patient-specific prognostic markers.

PCfun annotates protein complex function using natural language processing and machine learning, integrating word embeddings from PubMed Central articles with supervised and unsupervised methods.

NeuroSCORE scores 19,598 genes based on five CNS-related metrics, identifying 1,580 high-scoring genes significantly associated with CNS phenotypes.

This study evaluates the impact of multivariate normality assumptions in gene-set analysis, finding that non-normality affects performance, especially in mixed tumor subtypes.

FGSEA estimates preranked GSEA P-values down to 10^-100 with predictable error in seconds to minutes, recovering more FDR-significant pathways across 605 GEO datasets than the Broad implementation.

This study identifies gene network module changes during the vertebrate fin to limb transition by comparing zebrafish and mouse gene networks.

Martini uses SNP networks to enhance GWAS by identifying functionally related SNPs, improving discovery and interpretability.

RegEnrich identifies key gene regulators using a data-driven approach, revealing ETS transcription factors' role in interferon signaling.