{"title":"Protein-Protein Interaction Prediction: Recent Advances","authors":"M. Shatnawi","doi":"10.1109/DEXA.2017.30","DOIUrl":null,"url":null,"abstract":"Protein-protein interactions (PPI) occur at every level of cell functions. The identification of protein interactions provides a global picture of cellular functions and biological processes. It is also an essential step in the construction of PPI networks for human and other organisms. PPI prediction has been considered a promising alternative to the traditional drug design techniques. The identification of possible viral-host protein interaction can lead to a better understanding of infection mechanisms and, in turn, to the development of several medication drugs and treatment optimization. Several physiochemical experimental techniques have been applied to identify PPIs. However, these techniques are computationally expensive, significantly time consuming, and have covered only a small portion of the complete PPI networks. As a result, the need for computational techniques has been increased to validate experimental results and to predict non-discovered PPIs. This paper investigates and compares the recent computational PPI prediction approaches and discusses the technical challenges in this domain.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEXA.2017.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Protein-protein interactions (PPI) occur at every level of cell functions. The identification of protein interactions provides a global picture of cellular functions and biological processes. It is also an essential step in the construction of PPI networks for human and other organisms. PPI prediction has been considered a promising alternative to the traditional drug design techniques. The identification of possible viral-host protein interaction can lead to a better understanding of infection mechanisms and, in turn, to the development of several medication drugs and treatment optimization. Several physiochemical experimental techniques have been applied to identify PPIs. However, these techniques are computationally expensive, significantly time consuming, and have covered only a small portion of the complete PPI networks. As a result, the need for computational techniques has been increased to validate experimental results and to predict non-discovered PPIs. This paper investigates and compares the recent computational PPI prediction approaches and discusses the technical challenges in this domain.