MicroRNAs (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.

Giansanti, V., Castelli, M., Beretta, S., Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.31-44). Springer Verlag [10.1007/978-3-030-22744-9_3].

Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study

Giansanti V.;Castelli M.;Beretta S.;Merelli I.
2019

Abstract

MicroRNAs (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.
paper
Deep learning; Machine learning; miRNA; miRNA-target prediction;
English
19th International Conference on Computational Science, ICCS 2019
2019
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9783030227432
2019
11538
31
44
none
Giansanti, V., Castelli, M., Beretta, S., Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.31-44). Springer Verlag [10.1007/978-3-030-22744-9_3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/278969
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