{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T23:13:56Z","timestamp":1790291636942,"version":"4.1.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009247","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T00:00:00Z","timestamp":1628812800000}}],"reference-count":64,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T00:00:00Z","timestamp":1627948800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006545","name":"national institute on minority health and health disparities","doi-asserted-by":"publisher","award":["U54 MD007600"],"award-info":[{"award-number":["U54 MD007600"]}],"id":[{"id":"10.13039\/100006545","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucleotide-specific molecule. Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences. Thus, a novel machine learning-based approach was developed to support and simplify the early steps of the SELEX process, to help discriminate binding between DNA aptamers from those unspecified targets of DNA-binding sequences. An Artificial Intelligence (AI) approach to identify aptamers were implemented based on Natural Language Processing (NLP) and Machine Learning (ML). NLP method (CountVectorizer) was used to extract information from the nucleotide sequences. Four ML algorithms (Logistic Regression, Decision Tree, Gaussian Na\u00efve Bayes, Support Vector Machines) were trained using data from the NLP method along with sequence information. The best performing model was Support Vector Machines because it had the best ability to discriminate between positive and negative classes. In our model, an Accuracy (A) of 0.995, the fraction of samples that the model correctly classified, and an Area Under the Receiving Operating Curve (AUROC) of 0.998, the degree by which a model is capable of distinguishing between classes, were observed. The developed AI approach is useful to identify potential DNA aptamers to reduce the amount of rounds in a SELEX selection. This new approach could be applied in the design of DNA libraries and result in a more efficient and faster process for DNA aptamers to be chosen during SELEX.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009247","type":"journal-article","created":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T17:36:01Z","timestamp":1628012161000},"page":"e1009247","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":26,"title":["A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2704-6741","authenticated-orcid":true,"given":"Frances L.","family":"Heredia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2246-6744","authenticated-orcid":true,"given":"Abiel","family":"Roche-Lima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2734-9339","authenticated-orcid":true,"given":"Elsie I.","family":"Par\u00e9s-Matos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,8,3]]},"reference":[{"issue":"7","key":"pcbi.1009247.ref001","doi-asserted-by":"crossref","first-page":"16281","DOI":"10.3390\/s150716281","article-title":"Nucleic acid aptamers: An emerging tool for biotechnology and biomedical sensing","volume":"15","author":"TH Ku","year":"2015","journal-title":"Sensors (Basel)."},{"issue":"7","key":"pcbi.1009247.ref002","doi-asserted-by":"crossref","first-page":"3542","DOI":"10.1021\/acsomega.9b03860","article-title":"Aptamer-Based Sensing of Small Organic Molecules by Measuring Levitation Coordinate of Single Microsphere in Combined Acoustic\u2013Gravitational Field","volume":"5","author":"A Miyagawa","year":"2020","journal-title":"ACS Omega"},{"key":"pcbi.1009247.ref003","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fchem.2016.00014","article-title":"Small-Molecule Binding Aptamers: Selection Strategies, Characterization, and Applications","volume":"4","author":"A Ruscito","year":"2016","journal-title":"Front Chem"},{"issue":"2","key":"pcbi.1009247.ref004","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1038\/nrd1955","article-title":"Pegaptanib, a targeted anti-VEGF aptamer for ocular vascular disease","volume":"5","author":"EWM Ng","year":"2006","journal-title":"Nat Rev Drug Discov"},{"issue":"21","key":"pcbi.1009247.ref005","doi-asserted-by":"crossref","first-page":"13772","DOI":"10.1021\/acs.analchem.9b03210","article-title":"Development of DNA Aptamers against the Nucleocapsid Protein of Severe Fever with Thrombocytopenia Syndrome Virus for Diagnostic Application: Catalytic Signal Amplification using Replication Protein A-Conjugated Liposomes","volume":"91","author":"G Yeom","year":"2019","journal-title":"Anal Chem"},{"key":"pcbi.1009247.ref006","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.3389\/fmicb.2019.01462","article-title":"Application of Aptamers in Virus Detection and Antiviral Therapy","volume":"10","author":"X Zou","year":"2019","journal-title":"Front. 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