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Gene Ontology

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Using published pathway figures in enrichment analysis and machine learning.

BMC genomics
Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in...

Implementing link prediction in protein networks via feature fusion models based on graph neural networks.

Computational biology and chemistry
MOTIVATION: Protein-protein interactions serve as the cornerstone for various biochemical processes within biological organisms. Existing research methodologies predominantly employ link prediction techniques to analyze these interaction networks. Ho...

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.

Bioinformatics (Oxford, England)
MOTIVATION: Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associat...

Identification of Potential Neddylation-related Key Genes in Ischemic Stroke based on Machine Learning Methods.

Molecular neurobiology
Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even death. Understanding the molecular mechanisms involved in the occurrence and progression of IS is of great significance for developing effective treatme...

Molecular Investigation and Preliminary Validation of Candidate Genes Associated with Neurological Damage in Heat Stroke.

Molecular neurobiology
Heat stroke (HS) is a severe medical condition characterized by a systemic inflammatory response that may precipitate multi-organ dysfunction, with a particular predilection for inducing profound central nervous system impairments. We aim to employ b...

Feature engineering from meta-data for prediction of differentially expressed genes: An investigation of Mus musculus exposed to space-conditions.

Computational biology and chemistry
Transcription profiling is a key process that can reveal those biological mechanisms driving the response to various exposure conditions or gene perturbations. In this work, we investigate the prediction of differentially expressed genes (DEGs) when ...

DeepSS2GO: protein function prediction from secondary structure.

Briefings in bioinformatics
Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation hav...

Identification of Protein-Protein Interaction Associated Functions Based on Gene Ontology.

The protein journal
Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontolo...

Insights into the inner workings of transformer models for protein function prediction.

Bioinformatics (Oxford, England)
MOTIVATION: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent ...

Prediction of Drug-Target Interactions With High- Quality Negative Samples and a Network-Based Deep Learning Framework.

IEEE journal of biomedical and health informatics
Identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared to traditional experimental methods, computer-based methods for predicting DTIs can significantly reduce the time and financial burdens of drug develop...