Biology subjects

Nguyen, Q.-H.

Publications and source records attributed to Nguyen, Q.-H..

4 recordsLinked to original sources

bioRxiv2026

PrimeKG-Plus: a literature-derived expansion of a multimodal precision medicine knowledge graph

BackgroundDisease-centered knowledge graphs (KGs) support drug repurposing and precision medicine research, yet many remain static after release while primary databases and literature continue to expand. PrimeKG (Precision Medicine Knowledge Graph) is a widely used multimodal KG providing a holistic view of diseases. However, its public release reflects a June 2021 data cutoff, omitting several years of subsequent data growth. This lag is especially consequential for rare diseases, where mechanistic and therapeutic evidence often remains scattered across publications rather than structured resources. FindingWe present PrimeKG-Plus, a refreshed, rare-disease-enriched release of PrimeKG, rebuilt from all twenty original data resources updated to their December 2025 releases and three additional resources: OpenTargets, RepurposeDrugs, and nSIDES. Beyond synchronizing biomedical databases, PrimeKG-Plus captures approximately five years of previously unavailable rare-disease knowledge from the biomedical literature, curated from 637 PubMed abstracts and PubMed Central full-text articles using a language-model-assisted workflow focused on four rare neurological disorders: Canavan disease, Niemann-Pick disease type C, Tay-Sachs disease, and Batten disease. Extracted relations were refined through entity normalization, UMLS synonym mapping, embedding-based similarity ranking, and human expert review. Network topology analysis showed improved indirect drug-disease connectivity across three to six hops and added 447,288 drug-protein-disease paths linking previously unreachable drug-disease pairs. Temporal validation using drug approval records identified 55 molecular entities approved after the original PrimeKG June 2021 cutoff, 46 of which were absent from the original graph. ConclusionPrimeKG-Plus restores the temporal relevance of PrimeKG, providing an updated resource for drug repurposing, rare-disease research, and downstream machine-learning applications.

bioinformatics↗

bioRxiv2026

PAG-Agent: a biologist-oriented research assistant for context-aware pathway-level analysis and interpretation

Pathway analysis is a critical step for translating gene-level omics results into biological mechanisms, yet existing workflows often leave researchers with long lists of statistically significant pathways that are difficult to interpret, validate, and connect to experimental context. We developed PAG-Agent, a biologist-oriented virtual research assistant that integrates pathway-level statistical analysis, context-aware biological interpretation, literature-supported reasoning, and scientific writing support within a unified workflow. PAG-Agent supports bulk and single-cell transcriptomic data and enables users to perform data preprocessing, differential expression analysis, pathway analysis, pathway-level consensus analysis, and pathway-level meta-analysis through click-based and chat-based interactions. Unlike conventional pathway-analysis tools that analyze gene sets largely in isolation, PAG-Agent incorporates experimental conditions and research objectives to prioritize biologically relevant pathways and generate interpretable hypotheses. The system also provides gene and pathway annotation, citation retrieval, visualization, and writing refinement functions. In Alzheimers disease case studies using three transcriptomic datasets, PAG-Agent consistently identified neurodegeneration-related pathways across multiple analysis methods and datasets. In citation-retrieval benchmarking, PAG-Agent outperformed six competing LLMs across five common literature-support scenarios, demonstrating improved ability to provide contextually relevant and valid references. Overall, PAG-Agent lowers technical barriers for pathway-level analysis and helps researchers move from transcriptomic data to biologically grounded interpretation, hypothesis generation, and scientific communication.

bioinformatics↗

bioRxiv2022

A biphasic Deep Semi-supervised framework for Suptype Classification and biomarker discovery

To take full advantage of the unprecedented development of -omics technologies and generate further biological insights into human disease, it is a pressing need to develop novel computational methods for integrative analysis of multi-omics data. Here we proposed a biphasic Deep Semi-supervised multi-omics integration framework for Subtype Classification and biomarker discovery, DeepSSC. In phase 1, each denoising autoencoder was used to extract a compact representation for each -omics data, and then they were concatenated and put into a feed-forward neural network for subtype classification. In phase 2, our Biomarker Gene Identification procedure leveraged that neural network classifier to render subtype-specific important biomarkers. We also validated our given results on independent dataset. We demonstrated that DeepSSC exhibited better performance over other state-of-the-art techniques concerning classification tasks. As a result, DeepSSC successfully detected well-known biomarkers and hinted at novel candidates from different -omics data types related to the investigated biomedical problems.

bioinformatics↗

bioRxiv2021

overlappingCGM: Automatic detection and analysis of overlapping co-expressed gene modules

BackgroundWhen it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. ResultsThis study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. ConclusionsoCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM.

bioinformatics↗