Bioinformatics and Genomic Networks

ANYI: The ANnotated Yeast Interactome

ANYI integrates yeast PPI data with sixteen biophysical and regulatory annotations to enable cross-layer interactome analysis.

BiGER: Bayesian Rank Aggregation in Genomics with Extended Ranking Schemes

BiGER is a Bayesian rank aggregation method for meta-analysis of gene lists, handling mixed rankings and missing data with high accuracy.

Guidance for high-quality functional gene embeddings from large language models

This study evaluates gene embeddings from large language models and finds that functional descriptions yield high-quality, biologically meaningful representations.

Molecular maps of diseases from omics data and network embeddings

The paper constructs disease maps by integrating omics data with protein network embeddings, identifying more disease-relevant proteins than omics analysis alone.

A Unified Framework for Selecting and Evaluating Cell-Type-Specific Gene Co-expressions in Single-Cell Data

This study compares p-value and correlation strength for selecting gene pairs in single-cell data, finding p-values more robust for co-expression network estimation and benchmarking.

Constructing Gene Co-functional and Co-regulatory Networks from Public Transcriptomes using Condition-Specific Ensemble Co-expression

This paper introduces TEA-GCN, a method for constructing gene co-functional and co-regulatory networks from public RNA-seq data, showing improved performance and cross-species conservation.

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features

The paper evaluates hypergraph embedding methods for clustering metabolic hypergraphs to capture taxonomy-based features, finding that certain methods outperform others.

Pleiotropy and Disease Interactors: The Dual Nature of Genes Linking Ageing and Ageing-related Diseases

The paper identifies two gene interaction mechanisms linking ageing and ageing-related diseases using network analysis and machine learning.

Unified imputation of missing data modalities and features in multi-omic data via shared representation learning

MIMIR is a deep learning framework that imputes missing modalities and features in multi-omic data using shared representation learning, outperforming existing methods on pan-cancer data.

DANGO: Predicting higher-order genetic interactions

This paper introduces a computational method to predict higher-order genetic interactions, demonstrating its effectiveness in yeast by predicting trigenic interactions and uncovering biological functions related to cell growth.

Network-based integration of cross-dataset proteomic profiles using fold-change directionality

The paper integrates cross-dataset proteomic profiles using differential change directionality, identifying a central hub for doxorubicin treatment and associating proteome clusters with tumor stage.

Expanding P-NET, a multi-purpose biologically informed deep learning framework

The paper expands P-NET, a deep learning framework that uses biological pathways for genomic and transcriptomic predictions, showing competitive performance and interpretability.

Geometric Multidimensional Representation of Omic Signatures

The paper introduces a geometric framework to represent multi-omic signatures as multidimensional objects, revealing discordance and structural patterns in cancer metabolic circuitries.

Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations

This study compares neural network-based and other machine learning models for automated function prediction in yeast using co-expression data and updated Gene Ontology annotations, finding neural networks more accurate and robust.

BioTrendFinder - an interactive web tool for exploring functional drivers in gene- and protein-level bulk omics data

BioTrendFinder is a web tool that identifies functional drivers in bulk omics data by integrating sample-ranking, statistical results, and functional information for hypothesis-driven and exploratory analysis.

A residual-ratio framework for auditing transcriptomic gene signatures against background expression structure

This paper introduces a residual-ratio framework to audit transcriptomic gene signatures by quantifying their independence from background expression structure, showing it discriminates biological signatures from random gene sets across multiple cancer types.

Hyperbolic stratification of protein intrinsic disorder and structure-mediated interactions in the human protein interactome

The paper maps the human protein interactome onto a hyperbolic representation to analyze how intrinsic disorder and structure-mediated interactions influence network organization and molecular functions.

Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks

The paper integrates three computational methods to predict E. coli protein-protein interactions and functional networks, identifying over 20K high-confidence interactions.

GMIP-PLSR: A Nextflow Pipeline for GWAS and Multi-Omics Integration in Gene Prioritization Using PLSR

GMIP-PLSR improves GWAS gene prioritization by integrating multi-omics data using PLSR to address multicollinearity, outperforming PoPS in NAFLD case studies.

Benchmarking computational methods for multi-omics biomarker discovery in cancer

This paper benchmarks 20 computational methods for multi-omics biomarker discovery in cancer using real and simulated datasets, finding that DeePathNet and Deep-KEGG perform best.

GATSBI: Improving context-aware protein embeddingsthrough biologically motivated data splits

GATSBI improves protein embeddings by using biologically relevant data splits, showing better performance for understudied proteins in various tasks.

CLEAR: Concise List Enrichment Analysis Reducing Redundancy

CLEAR improves gene set enrichment analysis by jointly modeling gene sets with continuous statistics, reducing redundancy and improving sensitivity.

CyNET- a network analysis framework for high dimensionality, system level analyses of the functional Immunome

CyNET analyzes immune system networks to identify functional hubs and maturation patterns across age groups using single-cell transcriptomics.

Analysis of biological networks using Krylov subspace trajectories

The paper introduces a method using Krylov subspace trajectories for analyzing biological networks, demonstrating its application in community detection and perturbation analysis on the C. elegans neural network.

GO Big or Go Home: A New Gene Ontology Subset that Improves Plant Gene Function Prediction

The paper introduces GO Big, a new Gene Ontology subset for plants that improves gene function prediction by focusing on taxon-specific biological terms.

WayFindR: Investigating Feedback in Biological Pathways

WayFindR converts pathway data into graph structures to analyze feedback loops, revealing their underrepresentation in curated databases.

Common and rare genetic variants show network convergence for a majority of human traits

The paper shows common and rare genetic variants converge on shared molecular networks for most human traits, influenced by heritability and tissue specificity.

Triad-LMF: A Hierarchical Low-Rank Multimodal Fusion Framework for Robust Cancer Subtype Classification Using Multi-Omics Data

Triad-LMF improves cancer subtype classification by integrating multi-omics data through hierarchical low-rank multimodal fusion, showing better performance and interpretability.

Predicting Unseen Gene Perturbation Response Using Graph Neural Networks with Biological Priors

This paper introduces PerturbGraph, a graph neural network framework that predicts transcriptional responses to unseen gene perturbations by integrating biological priors, outperforming existing methods on benchmark datasets.

A graph-based learning approach to predict the effects of gene perturbations on molecular phenotypes

This paper introduces a graph-based machine learning approach to predict gene perturbation effects on molecular phenotypes using existing experimental data and biological networks.