Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics (IF 11.683)

YONES, CRISTIAN; BUGNON, LEANDRO A.; STEGMAYER, GEORGINA; BUGNON, LEANDRO A.; STEGMAYER, GEORGINA; MILONE, DIEGO H.; MILONE, DIEGO H.; YONES, CRISTIAN

IEEE Transactions on Neural Networks and Learning Systems

IEEE

Lugar: Piscataway; Año: 2020 vol. 31 p. 2857 - 2867

2162-237X

In the postgenome era, many problems in bioinfor-matics have arisen due to the generation of large amounts ofimbalanced data. In particular, the computational classificationof precursor microRNA (pre-miRNA) involves a high imbalancein the classes. For this task, a classifier is trained to identify RNAsequences having the highest chance of being miRNA precursors.The big issue is that well-known pre-miRNAs are usually just afew in comparison to the hundreds of thousands of candidatesequences in a genome, which results in highly imbalanceddata. This imbalance has a strong influence on most standardclassifiers and, if not properly addressed, the classifier is not ableto work properly in a real-life scenario. This work provides acomparative assessment of recent deep neural architectures fordealing with the large imbalanced data issue in the classificationof pre-miRNAs. We present and analyze recent architectures ina benchmark framework with genomes of animals and plants,with increasing imbalance ratios up to 1:2000. We also propose anew graphical way for comparing classifiers performance in thecontext of high-class imbalance. The comparative results obtainedshow that, at a very high imbalance, deep belief neural networkscan provide the best performance.