2021
DOI: 10.3389/fpubh.2021.821410
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Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA

Abstract: Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such pr… Show more

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Cited by 7 publications
(2 citation statements)
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“…In deep learning approaches, Al-Stouhi and Reddy [277] based on boosting to propose an instance-transfer model to reduce the class-imbalanced influence while also improving the performance by leveraging data from an auxiliary domain. Combining conventional DNNs with a Deep Decision Tree classifier, R. et al [278] proposes a Hybrid DNN architecture which addresses the class imbalance of certain RNA sequences by forcing the network to account for minor classes in the decision tree's hierarchical if-else cases. In addition to resorting to ensemble approaches, researchers can manage to resolve class-imbalanced problems through model parameters or training processes.…”
Section: Class-imbalanced Datamentioning
confidence: 99%
“…In deep learning approaches, Al-Stouhi and Reddy [277] based on boosting to propose an instance-transfer model to reduce the class-imbalanced influence while also improving the performance by leveraging data from an auxiliary domain. Combining conventional DNNs with a Deep Decision Tree classifier, R. et al [278] proposes a Hybrid DNN architecture which addresses the class imbalance of certain RNA sequences by forcing the network to account for minor classes in the decision tree's hierarchical if-else cases. In addition to resorting to ensemble approaches, researchers can manage to resolve class-imbalanced problems through model parameters or training processes.…”
Section: Class-imbalanced Datamentioning
confidence: 99%
“…In [28], the authors proposed an ensemble method based on moving the threshold that preserves the natural class distribution of the data. Other approaches, including deep learning, have also been applied to deal with imbalanced data [29].…”
Section: Literature Reviewmentioning
confidence: 99%