Best Paper Selection

Wang J, Liang H, Kang H, Gong Y. Understanding health information technology induced medication safety events by two conceptual frameworks. Appl Clin Inform 2019;10:158–67 https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0039-1678693 Lee JY, van Karnebeek CDM, Wasserman WW. Development and user evaluation of a rare disease gene prioritization workflow based on cognitive ergonomics. J Am Med Inform Assoc 2019;26(2):124–33 https://academic.oup.com/jamia/article/26/2/124/5235389 Patterson ES, Su G, Sarkar U. Reducing delays to diagnosis in ambulatory care settings: A macrocognition perspective. Appl Ergon 2020 Jan;82:102965 https://www.sciencedirect.com/science/article/pii/S0003687019301826?via%3Dihub

Heterogeneous networks integration for disease–gene prioritization with node kernels

Motivation The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems. Results We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease–gene associations and on a time-stamped benchmark containing 42 newly discovered associations. Availability and implementation Source code: https://github.com/dinhinfotech/DiGI.git. Supplementary information Supplementary data are available at Bioinformatics online.

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

ABSTRACTMotivationProper prioritization of candidate genes is essential to the genome-based diagnostics of a range of genetic diseases. However, it is a highly challenging task involving limited and noisy knowledge of genes, diseases and their associations. While a number of computational methods have been developed for the disease gene prioritization task, their performance is largely limited by manually crafted features, network topology, or pre-defined rules of data fusion.ResultsHere, we propose a novel graph convolutional network-based disease gene prioritization method, PGCN, through the systematic embedding of the heterogeneous network made by genes and diseases, as well as their individual features. The embedding learning model and the association prediction model are trained together in an end-to-end manner. We compared PGCN with five state-of-the-art methods on the Online Mendelian Inheritance in Man (OMIM) dataset for tasks to recover missing associations and discover associations between novel genes and diseases. Results show significant improvements of PGCN over the existing methods. We further demonstrate that our embedding has biological meaning and can capture functional groups of genes.AvailabilityThe main program and the data are available at https://github.com/lykaust15/Disease_gene_prioritization_GCN.

BaiHui: cross-species brain-specific network built with hundreds of hand-curated datasets

Motivation Functional gene networks, representing how likely two genes work in the same biological process, are important models for studying gene interactions in complex tissues. However, a limitation of the current network-building scheme is the lack of leveraging evidence from multiple model organisms as well as the lack of expert curation and quality control of the input genomic data. Results Here, we present BaiHui, a brain-specific functional gene network built by probabilistically integrating expertly-hand-curated (by reading original publications) heterogeneous and multi-species genomic data in human, mouse and rat brains. To facilitate the use of this network, we deployed a web server through which users can query their genes of interest, visualize the network, gain functional insight from enrichment analysis and download network data. We also illustrated how this network could be used to generate testable hypotheses on disease gene prioritization of brain disorders. Availability and implementation BaiHui is freely available at: http://guanlab.ccmb.med.umich.edu/BaiHui/. Supplementary information Supplementary data are available at Bioinformatics online.