Comparing Deep and Machine Learning Approaches in Bioinformatics

Bibliographic Details
Main Author: Giansanti, Valentina
Publication Date: 2019
Other Authors: Castelli, Mauro, Beretta, Stefano, Merelli, Ivan
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/152619
Summary: Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3
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spelling Comparing Deep and Machine Learning Approaches in BioinformaticsA miRNA-Target Prediction Case StudyDeep learningMachine learningmiRNAmiRNA-target predictionTheoretical Computer ScienceComputer Science(all)Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.Springer VerlagNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNGiansanti, ValentinaCastelli, MauroBeretta, StefanoMerelli, Ivan2023-05-11T22:03:31Z2019-01-012019-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion14application/pdfhttp://hdl.handle.net/10362/152619eng97830302274320302-9743PURE: 14032948https://doi.org/10.1007/978-3-030-22744-9_3info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:11:20Zoai:run.unl.pt:10362/152619Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:41:33.276472Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Comparing Deep and Machine Learning Approaches in Bioinformatics
A miRNA-Target Prediction Case Study
title Comparing Deep and Machine Learning Approaches in Bioinformatics
spellingShingle Comparing Deep and Machine Learning Approaches in Bioinformatics
Giansanti, Valentina
Deep learning
Machine learning
miRNA
miRNA-target prediction
Theoretical Computer Science
Computer Science(all)
title_short Comparing Deep and Machine Learning Approaches in Bioinformatics
title_full Comparing Deep and Machine Learning Approaches in Bioinformatics
title_fullStr Comparing Deep and Machine Learning Approaches in Bioinformatics
title_full_unstemmed Comparing Deep and Machine Learning Approaches in Bioinformatics
title_sort Comparing Deep and Machine Learning Approaches in Bioinformatics
author Giansanti, Valentina
author_facet Giansanti, Valentina
Castelli, Mauro
Beretta, Stefano
Merelli, Ivan
author_role author
author2 Castelli, Mauro
Beretta, Stefano
Merelli, Ivan
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Giansanti, Valentina
Castelli, Mauro
Beretta, Stefano
Merelli, Ivan
dc.subject.por.fl_str_mv Deep learning
Machine learning
miRNA
miRNA-target prediction
Theoretical Computer Science
Computer Science(all)
topic Deep learning
Machine learning
miRNA
miRNA-target prediction
Theoretical Computer Science
Computer Science(all)
description Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
2019-01-01T00:00:00Z
2023-05-11T22:03:31Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/152619
url http://hdl.handle.net/10362/152619
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9783030227432
0302-9743
PURE: 14032948
https://doi.org/10.1007/978-3-030-22744-9_3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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