A scalable random walk with restart on heterogeneous networks with Apache Spark for ranking disease-related genes through type-II fuzzy data fusion.
One of the effective missions of biology and medical science is to find disease-related genes. Recent research uses gene/protein networks to find such genes. Due to false positive interactions in these networks, the results often are not accurate and reliable. Integrating multiple gene/protein netwo...
| Publicado en: | Journal of Biomedical Informatics Vol. 115 |
|---|---|
| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Mar2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149313823&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149313823 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Mar2021 vid: 115 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 149313823 149313823 NLM33545331 149313823 10.1016/j.jbi.2021.103688 NLM33545331 149313823 ppct: 1 formats: tig: atl: A scalable random walk with restart on heterogeneous networks with Apache Spark for ranking disease-related genes through type-II fuzzy data fusion. aug: au: Joodaki, Mehdi Ghadiri, Nasser Maleki, Zeinab Lotfi Shahreza, Maryam affil: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran sug: subj: Bioinformatics Molecular Structure Algorithms Male APACHE (Acute Physiology and Chronic Health Evaluation) Male ab: One of the effective missions of biology and medical science is to find disease-related genes. Recent research uses gene/protein networks to find such genes. Due to false positive interactions in these networks, the results often are not accurate and reliable. Integrating multiple gene/protein networks could overcome this drawback, causing a network with fewer false positive interactions. The integration method plays a crucial role in the quality of the constructed network. In this paper, we integrate several sources to build a reliable heterogeneous network, i.e., a network that includes nodes of different types. Due to the different gene/protein sources, four gene-gene similarity networks are constructed first and integrated by applying the type-II fuzzy voter scheme. The resulting gene-gene network is linked to a disease-disease similarity network (as the outcome of integrating four sources) through a two-part disease-gene network. We propose a novel algorithm, namely random walk with restart on the heterogeneous network method with fuzzy fusion (RWRHN-FF). Through running RWRHN-FF over the heterogeneous network, disease-related genes are determined. Experimental results using the leave-one-out cross-validation indicate that RWRHN-FF outperforms existing methods. The proposed algorithm can be applied to find new genes for prostate, breast, gastric, and colon cancers. Since the RWRHN-FF algorithm converges slowly on large heterogeneous networks, we propose a parallel implementation of the RWRHN-FF algorithm on the Apache Spark platform for high-throughput and reliable network inference. Experiments run on heterogeneous networks of different sizes indicate faster convergence compared to other non-distributed modes of implementation. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|