A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.

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...

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Autores principales:	Frances L Heredia, Abiel Roche-Lima, Elsie I Parés-Matos
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/90b10effa3d343cc8031849e24ff62c6
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id	oai:doaj.org-article:90b10effa3d343cc8031849e24ff62c6
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spelling	oai:doaj.org-article:90b10effa3d343cc8031849e24ff62c62021-12-02T19:58:08ZA novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.1553-734X1553-735810.1371/journal.pcbi.1009247https://doaj.org/article/90b10effa3d343cc8031849e24ff62c62021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009247https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The 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 Naïve 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.Frances L HerediaAbiel Roche-LimaElsie I Parés-MatosPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009247 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Frances L Heredia Abiel Roche-Lima Elsie I Parés-Matos A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.
description	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 Naïve 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.
format	article
author	Frances L Heredia Abiel Roche-Lima Elsie I Parés-Matos
author_facet	Frances L Heredia Abiel Roche-Lima Elsie I Parés-Matos
author_sort	Frances L Heredia
title	A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.
title_short	A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.
title_full	A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.
title_fullStr	A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.
title_full_unstemmed	A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.
title_sort	novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/90b10effa3d343cc8031849e24ff62c6
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A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.

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