{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:43:00Z","timestamp":1761864180722,"version":"3.37.3"},"reference-count":9,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T00:00:00Z","timestamp":1602028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000199","name":"United States Department of Agriculture","doi-asserted-by":"publisher","award":["2016-70016-24781"],"award-info":[{"award-number":["2016-70016-24781"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Office of Research and Graduate Studies"},{"DOI":"10.13039\/100006630","name":"Utah State University","doi-asserted-by":"publisher","award":["#A40071"],"award-info":[{"award-number":["#A40071"]}],"id":[{"id":"10.13039\/100006630","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007199","name":"Utah Agricultural Experiment Station","doi-asserted-by":"publisher","award":["9411"],"award-info":[{"award-number":["9411"]}],"id":[{"id":"10.13039\/100007199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Understanding the mechanisms underlying infectious diseases is fundamental to develop prevention strategies. Host\u2013pathogen interactions (HPIs) are actively studied worldwide to find potential genomic targets for the development of novel drugs, vaccines and other therapeutics. Determining which proteins are involved in the interaction system behind an infectious process is the first step to develop an efficient disease control strategy. Very few computational methods have been implemented as web services to infer novel HPIs, and there is not a single framework which combines several of those approaches to produce and visualize a comprehensive analysis of HPIs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we introduce PredHPI, a powerful framework that integrates both the detection and visualization of interaction networks in a single web service, facilitating the apprehension of model and non-model host\u2013pathogen systems to aid the biologists in building hypotheses and designing appropriate experiments. PredHPI is built on high-performance computing resources on the backend capable of handling proteome-scale sequence data from both the host as well as pathogen. Data are displayed in an information-rich and interactive visualization, which can be further customized with user-defined layouts. We believe PredHPI will serve as an invaluable resource to diverse experimental biologists and will help advance the research in the understanding of complex infectious diseases.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>PredHPI tool is freely available at http:\/\/bioinfo.usu.edu\/PredHPI\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Sup \u00a0plementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa862","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T11:20:53Z","timestamp":1600773653000},"page":"622-624","source":"Crossref","is-referenced-by-count":14,"title":["PredHPI: an integrated web server platform for the detection and visualization of host\u2013pathogen interactions using sequence-based methods"],"prefix":"10.1093","volume":"37","author":[{"given":"Cristian D","family":"Loaiza","sequence":"first","affiliation":[{"name":"Bioinformatics Lab, Department of Plants, Soils and Climate , Logan, UT 84322, USA"},{"name":"Bioinformatics Facility, Center for Integrated BioSystems (CIB) , Logan, UT 84322, USA"}]},{"given":"Rakesh","family":"Kaundal","sequence":"additional","affiliation":[{"name":"Bioinformatics Lab, Department of Plants, Soils and Climate , Logan, UT 84322, USA"},{"name":"Bioinformatics Facility, Center for Integrated BioSystems (CIB) , Logan, UT 84322, USA"},{"name":"Department of Computer Science, Utah State University , Logan, UT 84322, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"2023051800332272900_btaa862-B1","doi-asserted-by":"crossref","first-page":"baw103","DOI":"10.1093\/database\/baw103","article-title":"HPIDB 2.0: a curated database for host\u2013pathogen interactions","volume":"2016","author":"Ammari","year":"2016","journal-title":"Database"},{"key":"2023051800332272900_btaa862-B2","doi-asserted-by":"crossref","first-page":"1850014","DOI":"10.1142\/S0219720018500142","article-title":"Training host\u2013pathogen protein\u2013protein interaction predictors","volume":"16","author":"Basit","year":"2018","journal-title":"J. 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