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Prediction of functional microexons by transfer learning
BACKGROUND: Microexons are a particular kind of exon of less than 30 nucleotides in length. More than 60% of annotated human microexons were found to have high levels of sequence conservation, suggesting their potential functions. There is thus a need to develop a method for predicting functional mi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627023/ https://www.ncbi.nlm.nih.gov/pubmed/34836511 http://dx.doi.org/10.1186/s12864-021-08187-9 |
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author | Cheng, Qi He, Bo Zhao, Chengkui Bi, Hongyuan Chen, Duojiao Han, Shuangze Gao, Haikuan Feng, Weixing |
author_facet | Cheng, Qi He, Bo Zhao, Chengkui Bi, Hongyuan Chen, Duojiao Han, Shuangze Gao, Haikuan Feng, Weixing |
author_sort | Cheng, Qi |
collection | PubMed |
description | BACKGROUND: Microexons are a particular kind of exon of less than 30 nucleotides in length. More than 60% of annotated human microexons were found to have high levels of sequence conservation, suggesting their potential functions. There is thus a need to develop a method for predicting functional microexons. RESULTS: Given the lack of a publicly available functional label for microexons, we employed a transfer learning skill called Transfer Component Analysis (TCA) to transfer the knowledge obtained from feature mapping for the prediction of functional microexons. To provide reference knowledge, microindels were chosen because of their similarities to microexons. Then, Support Vector Machine (SVM) was used to train a classification model in the newly built feature space for the functional microindels. With the trained model, functional microexons were predicted. We also built a tool based on this model to predict other functional microexons. We then used this tool to predict a total of 19 functional microexons reported in the literature. This approach successfully predicted 16 out of 19 samples, giving accuracy greater than 80%. CONCLUSIONS: In this study, we proposed a method for predicting functional microexons and applied it, with the predictive results being largely consistent with records in the literature. |
format | Online Article Text |
id | pubmed-8627023 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86270232021-11-30 Prediction of functional microexons by transfer learning Cheng, Qi He, Bo Zhao, Chengkui Bi, Hongyuan Chen, Duojiao Han, Shuangze Gao, Haikuan Feng, Weixing BMC Genomics Research BACKGROUND: Microexons are a particular kind of exon of less than 30 nucleotides in length. More than 60% of annotated human microexons were found to have high levels of sequence conservation, suggesting their potential functions. There is thus a need to develop a method for predicting functional microexons. RESULTS: Given the lack of a publicly available functional label for microexons, we employed a transfer learning skill called Transfer Component Analysis (TCA) to transfer the knowledge obtained from feature mapping for the prediction of functional microexons. To provide reference knowledge, microindels were chosen because of their similarities to microexons. Then, Support Vector Machine (SVM) was used to train a classification model in the newly built feature space for the functional microindels. With the trained model, functional microexons were predicted. We also built a tool based on this model to predict other functional microexons. We then used this tool to predict a total of 19 functional microexons reported in the literature. This approach successfully predicted 16 out of 19 samples, giving accuracy greater than 80%. CONCLUSIONS: In this study, we proposed a method for predicting functional microexons and applied it, with the predictive results being largely consistent with records in the literature. BioMed Central 2021-11-26 /pmc/articles/PMC8627023/ /pubmed/34836511 http://dx.doi.org/10.1186/s12864-021-08187-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Cheng, Qi He, Bo Zhao, Chengkui Bi, Hongyuan Chen, Duojiao Han, Shuangze Gao, Haikuan Feng, Weixing Prediction of functional microexons by transfer learning |
title | Prediction of functional microexons by transfer learning |
title_full | Prediction of functional microexons by transfer learning |
title_fullStr | Prediction of functional microexons by transfer learning |
title_full_unstemmed | Prediction of functional microexons by transfer learning |
title_short | Prediction of functional microexons by transfer learning |
title_sort | prediction of functional microexons by transfer learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627023/ https://www.ncbi.nlm.nih.gov/pubmed/34836511 http://dx.doi.org/10.1186/s12864-021-08187-9 |
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