DOI: https://doi.org/10.14710/jtsiskom.2020.13407

Model deep learning untuk klasifikasi fragmen metagenom dengan spaced k-mers sebagai ekstraksi fitur

Deep learning model for metagenome fragment classification using spaced k-mers feature extraction

1Department of Computer Science, IPB University, Indonesia

2Department of Agro-Industrial Engineering, IPB University, Indonesia

Received: 3 Jul 2019; Revised: 5 May 2020; Accepted: 25 May 2020; Available online: 3 Jul 2020; Published: 31 Jul 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
An open challenge in bioinformatics is the analysis of the sequenced metagenomes from the various environments. Several studies demonstrated bacteria classification at the genus level using k-mers as feature extraction where the highest value of k gives better accuracy but it is costly in terms of computational resources and computational time. Spaced k-mers method was used to extract the feature of the sequence using 111 1111 10001 where 1 was a match and 0 was the condition that could be a match or did not match. Currently, deep learning provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this research, two different deep learning architectures, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), trained to approach the taxonomic classification of metagenome data and spaced k-mers method for feature extraction. The result showed the DNN classifier reached 90.89 % and the CNN classifier reached 88.89 % accuracy at the genus level taxonomy.
Fulltext View|Download Email colleagues
Keywords: classification; deep learning; metagenomes; spaced k-mers
Funding: Institut Pertanian Bogor

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Related articles
Perbandingan Metode Ensemble Machine Learning untuk Klasifikasi Tenaga Kerja di Indonesia dengan Random Forest, XGBoost, dan CatBoost Decision support system model using FIS Mamdani for determining tire pressure K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes Maturity classification of cacao through spectrogram and convolutional neural network Automatic detection of crop diseases using gamma transformation for feature learning with a deep convolutional autoencoder More related articles
Most cited articles
Clothing size recommender on real-time fitting simulation using skeleton tracking and rigging Aplikasi Pendeteksi Kualitas Daging Menggunakan Segmentasi Region of Interest Berbasis Mobile Rancang Bangun Sistem Informasi Kampus Hijau Berbasis Web Pada JSN (Jaringan Sensor Nirkabel) Implementasi Jaringan Syaraf Tiruan Perambatan Balik untuk Memprediksi Harga Logam Mulia Emas Menggunakan Algoritma Lavenberg Marquardt Expert System for Diagnosis of Plant Pest and Disease Horticulture with Forward and Backward Chaining Inference More cited articles
  1. H. Wu, "PCA-based linear combinations of oligonucleotide frequencies for metagenomic DNA fragment binning," in IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Sun Valley, USA, Sept. 2008, pp. 46-53. doi: 10.1109/CIBCB.2008.4675758
  2. C. Simon and R. Daniel, "Metagenomic Analyses: Past and Future Trends," Applied and Environmental Microbiology, vol. 4, no. 77, pp. 1153-1161, 2011. doi: 10.1128/AEM.02345-10
  3. J. Qin, "A metagenome-wide association study of gut microbiota in type 2 diabetes," Nature, vol. 490, pp. 55-60, 2012. doi: 10.1038/nature11450
  4. P. J. Turnbaugh, R. E. Ley, M. A. Mahowald, V. Magrini, E. R. Mardis, and J. I. Gordon, "An obesity-associated gut microbiome with increased capacity for energy harvest," Nature, vol. 444, pp. 1027-1031, 2006. doi: 10.1038/nature05414
  5. J.-L. Bouchot, W. L. Trimble, G. Ditzler, Y. Lan, S. Essinger, and G. Rosen, "Advances in machine learning for processing and comparison of metagenomic data," in Computational Systems Biology, Molecular Mechanisms to Disease: Second Edition, Elsevier, pp. 295-329, 2013. doi: 10.1016/B978-0-12-405926-9.00014-9
  6. H. Zheng and H. Wu, "A novel LDA and PCA-based hierarchical scheme for metagenomic fragment binning," in IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Nashville, USA, Apr. 2009, pp. 53-59. doi: 10.1109/CIBCB.2009.4925707
  7. D. H. Huson, A. F. Auch, J. Qi, and S. C. Schuster, "MEGAN analysis of metagenomic data," Genome Research, vol. 17, no. 3, pp. 377-386, 2007. doi: 10.1101/gr.5969107
  8. A. C. McHardy and I. Rigoutsos, "What's in the mix: phylogenetic classification of metagenome sequence samples," Current Opinion in Microbiology, vol. 10, no. 5, pp. 499-503, 2007. doi: 10.1016/j.mib.2007.08.004
  9. W. A. Kusuma and Y. Akiyama, "Metagenome fragment binning based on characterization vector," in International Conference on Bioinformatics and Biomedical Technology, Sanya, China, Mar. 2011, pp. 1-5
  10. A. A. Pekuwali, W. A. Kusuma, and A. Buono, "Optimization of spaced k-mer frequency feature extraction using genetic algorithms for metagenome fragment classification," Journal of ICT Research and Applications, vol. 12, no. 2, pp. 123-137, 2018. doi: 10.5614/itbj.ict.res.appl.2018.12.2.2
  11. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015. doi: 10.1038/nature14539
  12. A. Fiannaca et al., "Deep learning models for bacteria taxonomic classification of metagenomic data," BMC Bioinformatics, vol. 19, no. 7, pp. 73-154, 2018. doi: 10.1186/s12859-018-2182-6
  13. P. Sunil, T. Rashmi, K. Vandana, and V. Pritish, "DeepInteract: deep neural network based protein-protein interaction prediction tool," Current Bioinformatic, vol. 12, no. 6, pp. 551-557, 2017. doi: 10.2174/1574893611666160815150746
  14. D. C. Richter, F. Ott, A. F. Auch, R. Schmid, and D. H. Huson, "MetaSim-A sequencing simulator for genomics and metagenomics," PLoS ONE, vol. 3, no. 10, pp. 417-412, 2008. doi: 10.1371/journal.pone.0003373
  15. B. M. J. Tromp and M. Li, "PatternHunter: faster and more sensitive homology search," Bioinformatics, vol. 18, no. 3, pp. 440-445, 2002. doi: 10.1093/bioinformatics/18.3.440
  16. S. Karsoliya, "Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture," International Journal of Engineering Trends and Technology, vol. 3, no. 6, pp. 714-717, 2012

Last update:

  1. A Deep Learning Model for 16S rRNA Classification with Taxonomic Tree Embedding

    Yue Jiang, Lin Shen, Don Adjeroh, Jie Lin. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021. doi: 10.1109/BIBM52615.2021.9669145

Last update: 2024-11-24 05:20:13

No citation recorded.