Classificação de Uma Classe para Detecção de PIWI-interacting RNAs
Resumo
A predição de PIWI-Interacting RNAs (piRNAs) é um tópico de interesse relacionado a pequenos RNAs não-codificantes, fornecendo pistas para a compreensão do mecanismo de geração de gametas. Várias abordagens de aprendizado de máquina foram propostas para a predição de piRNAs, mas ainda busca-se melhorias. Devido à grande variedade de RNAs não-codificantes, a escolha de quais deles serão utilizados como exemplos negativos pode ser uma tarefa trabalhosa. Em tais cenários, classificadores de uma classe podem ser uma opção. Assim, este trabalho investiga o poder de predição de classificadores de uma única classe em comparação aos classificadores binários para predição de piRNAs.
Palavras-chave:
PIWI-Interacting RNAs, RNAs não-codificantes, classificadores de uma classe, Bioinformática, Aprendizado de Máquina
Referências
Alashwal, H., Deris, S., and Othman, R. M. (2006). One-class support vector machines for protein-protein interactions prediction. International Journal of Biological and Medical Sciences, 1(2).
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.
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.
Publicado
15/10/2019
Como Citar
FERREIRA, Rafaela; CAMPOS, Adriano de; CERRI, Ricardo.
Classificação de Uma Classe para Detecção de PIWI-interacting RNAs. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (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.
