{"title":"OmicInt package: Exploring omics data and regulatory networks using integrative analyses and machine learning","authors":"Auste Kanapeckaite","doi":"10.1016/j.ailsci.2021.100025","DOIUrl":null,"url":null,"abstract":"<div><p><em>OmicInt</em> is an R software package developed for a user-friendly and in-depth exploration of significantly changed genes, gene expression patterns, and the associated epigenetic features as well as the related miRNA environment. In addition, <em>OmicInt</em> offers single cell RNA-seq and proteomics data integration to elucidate specific expression profiles. To achieve this, <em>OmicInt</em> builds on a novel scoring function capturing expression and pathology associations. The developed scoring function together with the implemented Gaussian mixture modelling pipline helps to explore genes and the linked interactome networks. The machine learning pipeline was designed to make the analyses straightforward for the non-experts so that researchers could take advantage of advanced analytics for their data evaluation. Additional functionalities, such as protein type and cellular location classification, provide useful assessments of the key interactors. The introduced package can aid in studying specific gene networks, understanding cellular perturbation events, and exploring interactions that might not be easily detectable otherwise. Thus, this robust set of bioinformatics tools can be very beneficial in drug discovery and target evaluation. <em>OmicInt</em> is designed to be freely accessible to involve a larger bioinformatics community and continuously improve the developed algorithmic methods.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318521000258/pdfft?md5=8a49e27739636c1b6dadd1e75978907a&pid=1-s2.0-S2667318521000258-main.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence in the life sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667318521000258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
OmicInt is an R software package developed for a user-friendly and in-depth exploration of significantly changed genes, gene expression patterns, and the associated epigenetic features as well as the related miRNA environment. In addition, OmicInt offers single cell RNA-seq and proteomics data integration to elucidate specific expression profiles. To achieve this, OmicInt builds on a novel scoring function capturing expression and pathology associations. The developed scoring function together with the implemented Gaussian mixture modelling pipline helps to explore genes and the linked interactome networks. The machine learning pipeline was designed to make the analyses straightforward for the non-experts so that researchers could take advantage of advanced analytics for their data evaluation. Additional functionalities, such as protein type and cellular location classification, provide useful assessments of the key interactors. The introduced package can aid in studying specific gene networks, understanding cellular perturbation events, and exploring interactions that might not be easily detectable otherwise. Thus, this robust set of bioinformatics tools can be very beneficial in drug discovery and target evaluation. OmicInt is designed to be freely accessible to involve a larger bioinformatics community and continuously improve the developed algorithmic methods.
Artificial intelligence in the life sciencesPharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)