Bioinformatics and Genomic Networks

Improved protein interaction models predict differences in complexes between human cell lines

The paper improves protein interaction prediction by addressing incomplete features and proteome coverage, identifying cell line-specific complexes.

Evaluating signaling pathway inference from kinase-substrate interactions and phosphoproteomics data

The paper evaluates methods for inferring kinase-driven signaling pathways using phosphoproteomics data and kinase-substrate networks, finding that literature-curated networks with network propagation best recover ground truth interactions.

Toden-E: Topology-Based and Density-Based Ensembled Clustering for the Development of Super-PAG in Functional Genomics using PAG Network and LLM

The paper introduces todenE, a clustering method combining topology and density-based approaches to group PAGs into Super-PAGs, improving functional representation in genomics.

Benchmarking Machine Learning Methods for Synthetic Lethality Prediction in Cancer

This study benchmarks machine learning methods for predicting synthetic lethality in cancer using matrix factorization and deep learning models, highlighting performance differences and emphasizing data and scenario importance.

Intrinsic structural disorder on proteins is involved in the interactome evolution

The paper uses mathematical models to show that intrinsic disorder in proteins correlates with clustering in eukaryotic interactomes and organism seniority.

E.PathDash, pathway activation analysis of publicly available pathogen gene expression data

E.PathDash provides a tool for analyzing gene expression data from pathogens, enabling rapid pathway activation analysis and reproducibility assessment across multiple studies.

mulea - an R package for enrichment analysis using multiple ontologies and empirical FDR correction

mulea provides an R package for enrichment analysis across multiple ontologies with empirical FDR correction to address limitations in traditional gene set enrichment methods.

FunCoup 6: advancing functional association networks across species with directed links and improved user experience

FunCoup 6 enhances functional association networks with directed links, expanded coverage, and improved tools for analyzing protein interactions across species.

PhenoMultiOmics: an enzymatic reaction inferred multi-omics network visualization web server

PhenoMultiOmics visualizes multi-omics networks around enzymatic reactions in cancer, revealing disrupted pathways and biomarkers through curated databases and analysis modules.

Semi-supervised Omics Factor Analysis (SOFA) disentangles known and latent sources of variation in multi-omic data

SOFA identifies novel sources of variation in multi-omic data by incorporating known covariates and focusing on unexplained factors.

FishCODE: a web-based information platform for comprehensive omics data exploration in fish research

FishCODE provides a comprehensive multi-omics platform for fish research, offering extensive datasets and annotations to enhance data exploration and analysis.

Deep Prediction of Human Essential Genes using Weighted Protein-Protein Interaction Networks

The paper proposes a deep learning model to predict human essential genes using weighted PPI networks and achieves better performance than centrality-based methods.

A Comprehensive App to Interpret and Visualize the Functional Analysis of KEGG Pathways and Gene Ontologies

The paper introduces a software tool for interpreting and visualizing KEGG pathway and Gene Ontology functional analysis, offering interactive networks, hierarchical analysis, and efficient processing.

Interpretable multi-omics integration with UMAP embeddings and density-based clustering

GAUDI integrates multi-omics data using UMAP embeddings and density-based clustering to uncover non-linear relationships and identify interpretable latent factors across datasets.

Usefulness of scRNA-seq data in predicting plant metabolic pathway genes

This study uses scRNA-seq data with machine learning to predict plant metabolic pathway genes, finding higher accuracy compared to bulk RNA-seq and suggesting more benchmark genes improve predictions.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

OmicsFootPrint integrates and interprets multi-omics data using circular images and deep neural networks, achieving high AUC scores in cancer subtype classification and drug response prediction.

Prediction and discovery of protein-protein direct interactions and stable complexes based on gene co-expression and co-evolution

This study develops a method to predict protein-protein direct interactions and stable complexes using co-expression and co-evolution data, achieving high AUROC scores and better performance than existing tools.

RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO

RummaGEO automatically mines gene sets from GEO RNA-seq studies for human and mouse, enabling signature search and metadata analysis.

A patient-centric knowledge graph approach to prioritize mutants for selective anti-cancer targeting

This paper introduces a knowledge graph to prioritize cancer mutations for targeted therapy by integrating clinical, functional, and structural data.

HELP: A computational framework for labelling and predicting human common and context-specific essential genes

The paper introduces HELP, a framework for labelling and predicting human essential genes, showing it outperforms existing methods in balancing false and true positive rates.

Proteome-wide copy-number estimation from transcriptomics

The paper estimates protein copy numbers from mRNA data across 369 cell lines and applies it to reclassify breast cancer subtypes and predict viral receptor abundances.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

This paper introduces derivative profiling (DP) to identify differentially changing signals in dynamic omics data, showing it is statistically comparable to existing methods and computationally efficient.

Paralog protein compensation preserves protein-protein interaction networks following gene loss in cancer

The paper shows that gene loss in cancer is often compensated by paralog protein abundance changes, stabilizing protein-protein interaction networks and creating therapeutic vulnerabilities.

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics

The paper customizes gene set collections for single-cell RNA-seq by incorporating cell type-specific gene expression data to improve gene set testing power and interpretability.

GNN4DM: A Graph Neural Network-based method to identify overlapping functional disease modules

GNN4DM identifies overlapping functional disease modules by integrating network topology and genomic data, outperforming existing methods and discovering novel multimorbidity modules.

Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology

This study identifies shared differentially expressed genes between COVID-19 and gastric cancer, revealing immune-related pathways and potential therapeutic agents.

MONFIT: Multi-omics factorization-based integration of time-series data sheds light on Parkinson's disease

MONFIT integrates time-series single-cell RNA-sequencing with bulk proteomics and metabolomics to identify PD-associated genes and pathways.

BioPAX-Explorer: a Python Object-Oriented framework for overcoming the complexity of querying biological networks

BioPAX-Explorer simplifies querying and analyzing BioPAX biological pathway data by providing an object-oriented Python framework with query capabilities and tools for data extraction and workflow development.

Visual Analysis of Multi-Omics Data

The paper introduces a tool for visualizing up to four omics datasets on organism-scale metabolic network diagrams using interactive web-based charts.

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data

G2PDeep-v2 is a web-based deep learning platform for phenotype prediction and biomarker discovery using multi-omics data across organisms, offering model training and visualization tools.