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Most of these cross\u2010talks are mediated via protein\u2010protein interactions between the host and the pathogen (HP\u2010PPI). Thus, it is essential to know how some pathogens interact with their hosts to understand the mechanism of infections. Malaria is a life\u2010threatening disease caused by an obligate intracellular parasite belonging to the <jats:italic>Plasmodium<\/jats:italic> genus, of which <jats:italic>P. falciparum<\/jats:italic> is the most prevalent. Several previous studies predicted human\u2010plasmodium protein\u2010protein interactions using computational methods have demonstrated their utility, accuracy, and efficiency to identify the interacting partners and therefore complementing experimental efforts to characterize host\u2010pathogen interaction networks. To predict potential putative HP\u2010PPIs, we use an integrative computational approach based on the combination of multiple OMICS\u2010based methods including human red blood cells (RBC) and <jats:italic>Plasmodium falciparum<\/jats:italic> 3D7 strain expressed proteins, domain\u2010domain based PPI, similarity of gene ontology terms, structure similarity method homology identification, and machine learning prediction. Our results reported a set of 716 protein interactions involving 302 human proteins and 130 Plasmodium proteins. This work provides a list of potential human\u2010<jats:italic>Plasmodium<\/jats:italic> interacting proteins. These findings will contribute to better understand the mechanisms underlying the molecular determinism of malaria disease and potentially to identify candidate pharmacological targets.<\/jats:p>","DOI":"10.1155\/2020\/2082540","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T17:36:07Z","timestamp":1608572167000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Integrative Computational Approach for the Prediction of Human\u2010<i>Plasmodium<\/i> Protein\u2010Protein 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