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To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http:\/\/ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https:\/\/github.com\/Superzchen\/iFeatureOmega-GUI\/ and https:\/\/github.com\/Superzchen\/iFeatureOmega-CLI\/.<\/jats:p>","DOI":"10.1093\/nar\/gkac351","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T19:11:49Z","timestamp":1650913909000},"page":"W434-W447","source":"Crossref","is-referenced-by-count":98,"title":["<i>iFeatureOmega:<\/i>an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets"],"prefix":"10.1093","volume":"50","author":[{"given":"Zhen","family":"Chen","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University , Zhengzhou 450046, China"},{"name":"Center for Crop Genome Engineering, Henan Agricultural University , Zhengzhou 450046, 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Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS) , Anyang\u00a0455000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7108-3574","authenticated-orcid":false,"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7749-0314","authenticated-orcid":false,"given":"Lukasz","family":"Kurgan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Commonwealth University , Richmond, VA, 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Natl. Acad. Sci. U.S.A."},{"key":"2022070423592761700_B60","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1021\/jm9700575","article-title":"New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids","volume":"41","author":"Sandberg","year":"1998","journal-title":"J. Med. Chem."},{"key":"2022070423592761700_B61","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0303-2647(90)90013-Q","article-title":"The global average DNA base composition of coding regions may be determined by the electron-ion interaction potential","volume":"23","author":"Lalovi\u0107","year":"1990","journal-title":"Biosystems"},{"key":"2022070423592761700_B62","first-page":"197","article-title":"A coding measure scheme employing electron-ion interaction pseudopotential (EIIP)","volume":"1","author":"Nair","year":"2006","journal-title":"Bioinformation"},{"key":"2022070423592761700_B63","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.3390\/cells8111332","article-title":"4mCpred-EL: An ensemble learning framework for identification of DNA N(4)-methylcytosine sites in the mouse genome","volume":"8","author":"Manavalan","year":"2019","journal-title":"Cells"},{"key":"2022070423592761700_B64","doi-asserted-by":"crossref","first-page":"4930","DOI":"10.1093\/bioinformatics\/btz408","article-title":"Iterative feature representations improve N4-methylcytosine site prediction","volume":"35","author":"Wei","year":"2019","journal-title":"Bioinformatics"},{"key":"2022070423592761700_B65","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1093\/bioinformatics\/btu820","article-title":"repDNA: a python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects","volume":"31","author":"Liu","year":"2015","journal-title":"Bioinformatics"},{"key":"2022070423592761700_B66","doi-asserted-by":"crossref","first-page":"W65","DOI":"10.1093\/nar\/gkv458","article-title":"Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences","volume":"43","author":"Liu","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"2022070423592761700_B67","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1517\/17460441.2016.1117070","article-title":"An overview of molecular fingerprint similarity search in virtual screening","volume":"11","author":"Muegge","year":"2016","journal-title":"Expert Opin. Drug Discov."},{"key":"2022070423592761700_B68","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.sbi.2008.05.007","article-title":"Exploring the structure and function paradigm","volume":"18","author":"Redfern","year":"2008","journal-title":"Curr. Opin. Struct. Biol."},{"key":"2022070423592761700_B69","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1002\/pro.5560040404","article-title":"Characterizing the microenvironment surrounding protein sites","volume":"4","author":"Bagley","year":"1995","journal-title":"Protein Sci"},{"key":"2022070423592761700_B70","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1002\/bip.360221211","article-title":"Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features","volume":"22","author":"Kabsch","year":"1983","journal-title":"Biopolymers"},{"key":"2022070423592761700_B71","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1093\/bioinformatics\/btp163","article-title":"Biopython: freely available python tools for computational molecular biology and bioinformatics","volume":"25","author":"Cock","year":"2009","journal-title":"Bioinformatics"},{"key":"2022070423592761700_B72","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1093\/bioinformatics\/btn222","article-title":"HSEpred: predict half-sphere exposure from protein sequences","volume":"24","author":"Song","year":"2008","journal-title":"Bioinformatics"},{"key":"2022070423592761700_B73","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1002\/(SICI)1097-0282(199603)38:3<305::AID-BIP4>3.0.CO;2-Y","article-title":"Reduced surface: an efficient way to compute molecular surfaces","volume":"38","author":"Sanner","year":"1996","journal-title":"Biopolymers"},{"key":"2022070423592761700_B74","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recogn. Lett."},{"key":"2022070423592761700_B75","first-page":"226","volume-title":"Proceedings of the Second International Conference on Knowledge Discovery and Data Mining","author":"Ester","year":"1996"},{"key":"2022070423592761700_B76","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"LIII. On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"London Edinburgh Dublin Philos. Mag. J. Sci."},{"key":"2022070423592761700_B77","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: a 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"2022070423592761700_B78","doi-asserted-by":"crossref","first-page":"51","DOI":"10.25080\/Majora-92bf1922-00a","article-title":"Data structures for statistical computing in python","volume-title":"Proceedings of the 9th Python in Science Conference","author":"McKinney","year":"2010"},{"key":"2022070423592761700_B79","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"2022070423592761700_B80","doi-asserted-by":"crossref","DOI":"10.25080\/TCWV9851","volume-title":"Exploring Network Structure, Dynamics, and Function Using NetworkX","author":"Hagberg","year":"2008"},{"key":"2022070423592761700_B81","doi-asserted-by":"crossref","first-page":"D364","DOI":"10.1093\/nar\/gku1028","article-title":"A series of PDB-related databanks for everyday needs","volume":"43","author":"Touw","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"2022070423592761700_B82","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1002\/prot.21135","article-title":"Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks","volume":"65","author":"Passerini","year":"2006","journal-title":"Proteins"},{"key":"2022070423592761700_B83","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1093\/bib\/bby065","article-title":"LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property","volume":"20","author":"Han","year":"2019","journal-title":"Brief Bioinform."},{"key":"2022070423592761700_B84","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13321-019-0355-6","article-title":"An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor","volume":"11","author":"Liu","year":"2019","journal-title":"J. Cheminform."},{"key":"2022070423592761700_B85","first-page":"2825","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Scikit-learn: Machine Learning in Python"},{"key":"2022070423592761700_B86","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The protein data bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"2022070423592761700_B87","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.1093\/bioinformatics\/bth261","article-title":"Data mining in bioinformatics using weka","volume":"20","author":"Frank","year":"2004","journal-title":"Bioinformatics"},{"key":"2022070423592761700_B88","doi-asserted-by":"crossref","first-page":"4768","DOI":"10.1021\/bi963091e","article-title":"The structure of the cytidine deaminase-product complex provides evidence for efficient proton transfer and ground-state destabilization","volume":"36","author":"Xiang","year":"1997","journal-title":"Biochemistry"},{"key":"2022070423592761700_B89","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1006\/jmbi.1998.2511","article-title":"Crystallographic and kinetic investigations on the mechanism of 6-pyruvoyl tetrahydropterin synthase","volume":"286","author":"Ploom","year":"1999","journal-title":"J. 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