Maxat Kulmanov

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

METEOR integrates enzyme prediction with genome-scale metabolic model reconstruction to improve functional annotations and network viability.

This study identifies a novel mechanism of phase separation driven by the Janus-class KFL region of PYK2, showing its role in kinase activation and cell adhesion.

This study uses whole-genome sequencing to analyze S. aureus isolates from Saudi Arabia, revealing higher genetic diversity and antimicrobial resistance in pilgrimage cities compared to non-pilgrimage cities.

The paper constructs a pangenome graph reference for Japanese and Saudi Arabian populations, showing improved variant detection compared to linear references and comparable performance to existing pangenome graphs.

The paper evaluates genome-scale protein function annotations for completeness, coherence, and consistency, finding that computational methods fail to produce biologically plausible annotations despite high individual protein accuracy.

The paper develops a transformer-based model to predict protein functions in bacterial genomes using genomic context, showing that synteny significantly improves prediction performance.

This paper introduces PU-GO, a method for protein function prediction that addresses the positive-unlabeled ranking problem using ontology-based priors, showing improved performance on benchmark datasets.

DeepGOMeta predicts protein functions for microbes using a deep learning model trained on microbial data, improving function prediction for novel proteins.

DeepGO-SE improves protein function prediction for distantly related proteins by using a pretrained large language model and a neuro-symbolic model that leverages GO axioms.

The paper presents a fully annotated reference genome from a Saudi individual using de novo assembly and AI-based annotation, making all data publicly available.

DeepGOZero improves protein function prediction by using ontology axioms and zero-shot learning to predict functions with few or no annotations.

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

DeepPheno predicts phenotypes from single gene loss-of-function using a hierarchical classification approach, outperforming existing methods and enabling gene-disease association predictions.

The paper identifies the envelope protein E as a therapeutic target for COVID-19 and suggests existing drugs that inhibit this protein could prevent ARDS.

This paper reviews methods that integrate biomedical ontologies with machine learning to improve similarity computation and model performance using ontology embeddings and constraints.

DeepGOPlus improves protein function prediction from sequence alone, achieving faster and more accurate results on biological processes and cellular components.

PathoPhenoDB links human pathogens to disease phenotypes to support infectious disease research by integrating pathogen-disease associations and drug resistance information.

DeepPVP prioritizes causative variants using deep learning and phenotype similarity, outperforming existing methods in speed and accuracy.

Vec2SPARQL integrates vector space representations with SPARQL queries to enable semantic similarity and classification within a single query for biomedical applications.

OligoPVP identifies oligogenic disease variants by integrating patient phenotypes and gene interactions in whole exome or genome sequences, showing improved performance over existing methods.

The paper develops an ontology-based method to validate consistency between function and phenotype annotations and predicts phenotypes from functions, improving annotation quality and enabling new biological insights.