http://dx.doi.org/10.3389/fbinf.2025.1727953

Background
Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among entities (e.g., genes, diseases, proteins), providing valuable insights for research. However, these relationships are typically derived from prior studies, leaving potential unknown associations unexplored. Identifying such unknown associations, including previously unknown disease-associated genes, remains a critical challenge in bioinformatics and is crucial for advancing biomedical knowledge.


Methods
Traditional methods, such as linkage analysis and genome-wide association studies (GWAS), can be time-consuming and resource-intensive. This highlights the need for efficient computational approaches to identify or predict new genes using known disease-gene associations. Recently, network-based methods and KGs, enhanced by advances in machine learning (ML) frameworks, have emerged as promising tools for inferring these unexplored associations. Given the technical limitations of the Neo4j Graph Data Science (GDS) machine learning pipeline, we developed a novel machine learning pipeline called KG2ML (Knowledge Graph to Machine Learning). This pipeline utilizes our Positive and Unlabeled (PU) learning algorithm, PULSCAR (Positive Unlabeled Learning Selected Completely At Random), and incorporates path-based feature extraction from ProteinGraphML.


Results
KG2ML was applied to 12 diseases, including Bipolar Disorder, Coronary Artery Disease, and Parkinson’s Disease, to infer disease-associated genes not explicitly recorded in DDKG. For several of these diseases, 14 out of the 15 top-ranked genes lacked prior explicit associations in the DDKG but were supported by literature and TINX (Target Importance and Novelty Explorer) evidence. Incorporating PULSCAR-imputed genes as positives enhanced XGBoost classification, demonstrating the potential of PU learning in identifying hidden gene-disease relationships.


Conclusion
The observed improvement in classification performance after the inclusion of PULSCAR-imputed genes as positive examples, along with the subject matter experts’ (SME) evaluations of the top 15 imputed genes for 12 diseases, suggests that PU learning can effectively uncover disease-gene associations missing from existing knowledge graphs (KGs). By integrating KG data with ML-based inference, our KG2ML pipeline provides a scalable and interpretable framework to advance biomedical research while addressing the inherent limitations of current KGs.

2026-01-08

2026-01-09

https://doi.org/10.1145/3765612.3767225

2025-12-10

2026-01-09

http://dx.doi.org/10.1101/2025.08.11.666099

Abstract
The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

2026-01-08

2026-01-09

https://doi.org/10.7717/peerj.17470

TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X's predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user's web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.

2026-01-08

2026-01-09

http://dx.doi.org/10.1016/j.drudis.2024.103882

2026-01-08

2026-01-08

https://doi.org/10.1093/nar/gkac1033

2022-11-29

2023-01-08

http://dx.doi.org/10.1002/cpz1.355

2022-09-12

2022-09-12

http://dx.doi.org/10.1016/j.bpj.2014.09.039

2022-09-12

2022-09-12

http://dx.doi.org/10.1063/1.4867286

2022-09-12

2022-09-12

http://dx.doi.org/10.1073/pnas.1309755110

G-protein-coupled receptors (GPCRs) mediate cellular responses to various hormones and neurotransmitters and are important targets for treating a wide spectrum of diseases. Although significant advances have been made in structural studies of GPCRs, details of their activation mechanism remain unclear. The X-ray crystal structure of the M2 muscarinic receptor, a key GPCR that regulates human heart rate and contractile forces of cardiomyocytes, was determined recently in an inactive antagonist-bound state. Here, activation of the M2 receptor is directly observed via accelerated molecular dynamics simulation, in contrast to previous microsecond-timescale conventional molecular dynamics simulations in which the receptor remained inactive. Receptor activation is characterized by formation of a Tyr206
5.58
–Tyr440
7.53
hydrogen bond and ∼6-Å outward tilting of the cytoplasmic end of transmembrane α-helix 6, preceded by relocation of Trp400
6.48
toward Phe195
5.47
and Val199
5.51
and flipping of Tyr430
7.43
away from the ligand-binding cavity. Network analysis reveals that communication in the intracellular domains is greatly weakened during activation of the receptor. Together with the finding that residue motions in the ligand-binding and G-protein-coupling sites of the apo receptor are correlated, this result highlights a dynamic network for allosteric regulation of the M2 receptor activation.

2022-09-12

2022-09-12

http://dx.doi.org/10.1002/pro.2129

2022-09-12

2022-09-12

http://dx.doi.org/10.1371/journal.pcbi.1002015

2022-09-12

2026-01-09