Bioinformatics and Genomic Networks

Evaluating the Influence of Disease-Gene Associations in the Significance of Disease Modules through the lens of Network Medicine

This study examines how disease module significance depends on module size and Gene-Disease Association scores, suggesting cutoffs for robust module construction.

Regulation Flow Analysis discovers molecular mechanisms of action from large knowledge databases

This paper introduces Regulation Flow Analysis (RFA), a method that generates causal hypotheses of drug mechanisms using knowledge graphs, improving understanding of biological processes and drug development.

NetMedPy: A Python package for Large-Scale Network Medicine Screening

NetMedPy provides a computational package for efficient Network Medicine analyses, supporting diverse scoring functions and metrics for large-scale molecular screening.

DiCE: Differential Centrality-Ensemble Analysis Based on Gene Expression Profiles and Protein-Protein Interaction Network

DiCE identifies disease-associated genes by combining differential expression with network centrality analysis, validated on prostate cancer datasets to find genes correlated with disease-free survival.

Generative prediction of causal gene sets responsible for complex traits

The paper develops TWAVE, a generative model that identifies causal gene sets for complex traits by integrating transcriptomic data and gene perturbation responses.

Network architecture of transcriptomic stress responses in zebrafish embryos

The paper examines the relationship between transcriptomic stress responses and PPI network architecture in zebrafish embryos, finding stress response genes are more central in the network.

D-CoMEx: A Unified Approach to Identify Phenotype-specific miRNA Biomarkers Related to Diseases

D-CoMEx identifies phenotype-specific miRNA biomarkers linked to Parkinson's disease and Breast Cancer by integrating co-expression networks with differential analysis.

iTissue

The paper introduces iTissue, a method integrating spatial and single-cell transcriptomics with molecular networks to identify metastasis-related genes in ccRCC.

CladeOScope-GSA: A Tool for Predicting Functional Gene Interactions via Phylogenetic Profiling

CladeOScope-GSA predicts functional gene interactions using phylogenetic profiling across 1905 species, offering enhanced analysis and visualization tools for comparative genomics.

Circular van Krevelen diagram for visualizing metabolic pathways

The paper introduces a circular van Krevelen diagram for automated metabolic pathway visualization, improving consistency for data interpretation and map creation.

A Gene Set Foundation Model Pre-Trained on a Massive Collection of Diverse Gene Sets

The paper introduces a gene set foundation model trained on diverse gene sets to predict gene functions, achieving superior performance compared to existing methods.

VerteBrain reveals novel neural and non-neural protein assemblies conserved across vertebrate evolution

VerteBrain maps conserved brain protein interactions across five vertebrate species, revealing novel complexes and disease mechanisms.

Gene Interaction Network Architecture of Human Polygenic Traits Reveals Domain-Specific Connectivity and Evolutionary Pressures

The paper analyzes gene interaction networks across polygenic traits, finding higher connectivity in trait-associated genes and weaker selective constraints in highly connected networks, and develops an online portal for hypothesis generation.

Trends Assessment of the Genetic Mutation Induced Protein-Protein Interaction Variation via Protein Large Language Driven Method

This paper introduces TAPPI, a deep learning framework that assesses trends in protein-protein interaction variations caused by genetic mutations, achieving state-of-the-art performance and biological relevance.

Gene set optimization for cancer transcriptomics using sparse principal component analysis

This paper optimizes gene set collections for cancer transcriptomics using sparse PCA, improving survival associations across 21 solid cancers.

Data-driven extraction of human kinase-substrate relationships from omics datasets

This study develops a machine learning model to predict kinase-substrate relationships from omics data, improving upon existing methods and providing a web tool for analysis.

Case Study of Using AI as Co-Pilot in Biotech Research: Functional Network Analysis of Invasive Cancer

This paper explores using AI as a co-pilot in biotech research for analyzing functional protein networks in invasive colorectal cancer, finding that AI improves efficiency but requires expert guidance and cannot fully automate complex tasks.

Integrating genetic and gene expression data in network-based stratification analysis of cancers

This study integrates somatic mutation and RNA sequencing data using network-based stratification to identify cancer subtypes associated with survival and histology, revealing key driver genes.

Data- and knowledge-derived functional landscape of human solute carriers

The paper creates a functional landscape of human solute carriers by integrating multi-omics data and curated knowledge, assigning functions to each SLC and providing a web-accessible resource.

Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach.

The paper predicts 196 new candidate proteins and identifies 14 sub-modules and hub proteins involved in rice seed development using an ensemble network-based approach.

Improving plant functional annotation from knowledge graphs using Graph Neural Networks

This paper develops a knowledge graph and uses graph neural networks to improve gene functional annotation in plants, outperforming orthology-based methods in transferring knowledge between species.

Longitudinal pathway analysis using structural information with case studies in early type 1 diabetes

The paper introduces a new pathway analysis method for longitudinal data, demonstrating its application in early type 1 diabetes studies with subtle biological signals.

A multilayer network approach elucidates time- and tissue-specific developmental and aging processes

The paper constructs and integrates protein-protein interaction networks across time and tissues to identify protein groups and networks associated with development and aging, and ranks anti-aging drug candidates.

From Genes to Pathways: A Curated Gene Approach to Accurate Pathway Reconstruction in Teleost Fish Transcriptomics

The paper presents a manually curated gene list for teleost fish transcriptomics to improve pathway reconstruction accuracy in non-model organisms.

Systematic discovery of single-cell protein networks in cancer with Shusi

Shusi predicts context-specific protein-protein interactions in cancer using a large language model-enhanced variational graph auto-encoder, identifying actionable vulnerabilities in acute myeloid leukemia.

Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes

This study introduces DGP-AMIO, a graph attention network-based method that integrates multi-omics and gene interaction data to identify novel disease genes, achieving high performance across 20 disease datasets.

GOALS: Gene Ontology Analysis with Layered Shells for Enhanced Functional Insight and Visualization

GOALS introduces a novel framework for analyzing Gene Ontologies by discretizing them into optimal layers to enhance functional insights and visualization in biological processes.

MiT4SL: multi-omics triplet representation learning for cancer cell line-adapted prediction of synthetic lethality

MiT4SL predicts synthetic lethality in cancer cell lines using multi-omics triplet representation learning, outperforming existing models in cell line-adapted scenarios.

ProtHGT: Heterogeneous Graph Transformers for Automated Protein Function Prediction Using Biological Knowledge Graphs and Language Models

ProtHGT predicts protein functions using a heterogeneous graph transformer that integrates biological knowledge graphs and language models, achieving state-of-the-art performance across GO sub-ontologies.

DNA damage-associated protein co-expression network in cardiomyocytes informs on tolerance to genetic variation and disease

The paper identifies DNA damage-associated protein co-expression modules in cardiomyocytes and shows their relationship to genetic variation tolerance and CVD risk.