Apr 9, 2020

Investigating Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

BioRxiv : the Preprint Server for Biology
A. H. Da CostaR. A. C. Santos

Abstract

PIWI-Interacting RNAs (piRNAs) form an important class of non-coding RNAs that play a key role in the genome integrity through the silencing of transposable elements. However, despite their importance and the large application of deep learning in computational biology for classification tasks, there are few studies of deep learning and neural networks for piRNAs prediction. Therefore, this paper presents an investigation on deep feedforward networks models for classification of transposon-derived piRNAs. We analyze and compare the results of the neural networks in different hyperparameters choices, such as number of layers, activation functions and optimizers, clarifying the advantages and disadvantages of each configuration. From this analysis, we propose a model for human piRNAs classification and compare our method with the state-of-the-art deep neural network for piRNA prediction in the literature and also traditional machine learning algorithms, such as Support Vector Machines and Random Forests, showing that our model has achieved a great performance with an F-measure value of $0.872$, outperforming the state-of-the-art method in the literature.

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Observational Study
Patterns
Classification
Trees (plant)
Sequence Analysis
Species
Analysis
Biological Evolution
VPS52

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

Current Biology : CB
Bo W Han, Phillip D Zamore
International Journal of Human Genetics
Lingjun ZuoXingguang Luo
© 2020 Meta ULC. All rights reserved