One Class Classification to Detect PIWI-interacting RNAs
Abstract
The prediction of PIWI-Interacting RNAs (piRNAs) is a topic of interest related to small non-code RNAs, providing clues to understanding the mechanism of gametes generation. Several machine learning approaches have been proposed for the prediction of piRNAs, but improvements are still being sought. Because of the wide variety of non-coding RNAs, choosing which of them will be used as negative examples can be a hard task. In such scenarios, one class classifiers can be an option. Thus, this paper investigates the predictive power of one class classifiers in comparison with binary classifiers to predict piRNAs.
Keywords:
PIWI-Interacting RNAs, non-coding RNAs, one class classifiers, Bioinformatics, Machine Learning
References
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Zhang, Y., Wang, X., and Kang, L. (2011). A k-mer scheme to predict pirnas and characterize locust pirnas. Bioinformatics, 27(6):771–776.
Carmen, L., Michela, B., Rosaria, V., Gabriella, M., et al. (2009). Existence of snorna, microrna, pirna characteristics in a novel non-coding rna: x-ncrna and its biological implication in homo sapiens. Journal of Bioinformatics and Sequence Analysis, 1(2):031–040.
Chollet, F. et al. (2015). Keras. https://keras.io.
Claverie, J.-M. (2005). Fewer genes, more noncoding rna. Science, 309(5740):1529– 1530.
Cox, D. N., Chao, A., Baker, J., Chang, L., Qiao, D., and Lin, H. (1998). A novel class of evolutionarily conserved genes defined by piwi are essential for stem cell self-renewal. Genes & development, 12(23):3715–3727.
Falbel, D. (2018). Tensorflow for r: Predicting fraud with autoencoders and keras.
Hirakata, S. and Siomi, M. C. (2016). pirna biogenesis in the germline: from transcription of pirna genomic sources to pirna maturation. Biochimica et Biophysica Acta (BBA)Gene Regulatory Mechanisms, 1859(1):82–92.
Huang, Y., Liu, N., Wang, J. P., Wang, Y. Q., Yu, X. L., Wang, Z. B., Cheng, X. C., and Zou, Q. (2012). Regulatory long non-coding rna and its functions. Journal of physiology and biochemistry, 68(4):611–618.
Iwasaki, Y. W., Siomi, M. C., and Siomi, H. (2015). Piwi-interacting rna: its biogenesis and functions. Annual review of biochemistry, 84:405–433.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, volume 112. Springer.
Khan, S. S. and Madden, M. G. (2014). One-class classification: taxonomy of study and review of techniques. The Knowledge Engineering Review, 29(3):345–374.
Liu, B., Wu, H., and Chou, K.-C. (2017). Pse-in-one 2.0: An improved package of web servers for generating various modes of pseudo components of dna, rna, and protein sequences. 09:67–91.
Luo, L., Li, D., Zhang, W., Tu, S., Zhu, X., and Tian, G. (2016). Accurate prediction of transposon-derived pirnas by integrating various sequential and physicochemical features. PloS one, 11(4):e0153268.
Manevitz, L. M. and Yousef, M. (2001). One-class svms for document classification. Journal of machine Learning research, 2(Dec):139–154.
Mattick, J. S. (2005). The functional genomics of noncoding rna. Science, 309(5740):1527–1528.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural computation, 13(7):1443–1471.
Spinosa, E. J. and de Carvalho, A. C. (2005). Combining one-class classifiers for robust novelty detection in gene expression data. In Brazilian Symposium on Bioinformatics, pages 54–64. Springer.
Xie, C., Yuan, J., Li, H., Li, M., Zhao, G., Bu, D., Zhu, W., Wu, W., Chen, R., and Zhao, Y. (2013). Noncodev4: exploring the world of long non-coding rna genes. Nucleic acids research, 42(D1):D98–D103.
Zhang, Y., Wang, X., and Kang, L. (2011). A k-mer scheme to predict pirnas and characterize locust pirnas. Bioinformatics, 27(6):771–776.
Published
2019-10-15
How to Cite
FERREIRA, Rafaela; CAMPOS, Adriano de; CERRI, Ricardo.
One Class Classification to Detect PIWI-interacting RNAs. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 741-752.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9330.
