Background: Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine.
Basic Procedures: We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing.
Main Findings: We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.
Principal Conclusions: We show the benchmarking results of novel blockchain-based methods for institutions to share patient outcomes related to gene-drug interactions. Our findings support blockchain utilization in healthcare, genomic and biomedical applications. The source code is publicly available at https://github.com/tsungtingkuo/genedrug.
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http://dx.doi.org/10.1016/j.ijmedinf.2021.104559 | DOI Listing |
Bioinformatics
January 2025
College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
Motivation: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph motifs of (disease, drug, gene) triplets. Among them, the triangle is a steady and important motif structure in the network, and other various motifs different from the triangle also indicate rich semantic relationships.
View Article and Find Full Text PDFEur J Breast Health
January 2025
Department of Biomedical Engineering, Faculty of Engineering, İzmir University of Economics, İzmir, Turkey.
Discov Oncol
December 2024
Dr B R Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India.
Cancer, a leading cause of death worldwide, is projected to increase by 76.6% in new cases and 89.7% in mortality by 2050 (WHO 2022).
View Article and Find Full Text PDFDiscov Oncol
December 2024
School of Clinical Medicine, Dali University, Dali, 671000, Yunnan, People's Republic of China.
Objective: Searching for potential biomarkers and therapeutic targets for early diagnosis of gynecological tumors to improve patient survival.
Methods: Microarray datasets of cervical cancer (CC) and ovarian cancer (OC) were downloaded from the Gene Expression Omnibus (GEO) database, then, differential gene expression between cancerous and normal tissues in the datasets was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to screen for co-expression modules associated with CC and OC.
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