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WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs

Akbari Rokn Abadi, S ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1371/journal.pone.0267106
  3. Publisher: Public Library of Science , 2022
  4. Abstract:
  5. The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing customized network structures. However, a few works focus on more effective factors, such as input encoding method or implementation technology, to address accuracy and efficiency issues in this area. Therefore, in this work, we propose an image-based encoding method, called as WalkIm, whose adoption, even in a simple neural network, provides competitive accuracy and superior efficiency, compared to the existing classification methods (e.g. VGDC, CASTOR, and DLMCNN) for a variety of biological sequences. Using WalkIm for classifying various data sets (i.e. viruses whole-genome data, metagenomics read data, and metabarcoding data), it achieves the same performance as the existing methods, with no enforcement of parameter initialization or network architecture adjustment for each data set. It is worth noting that even in the case of classifying high-mutant data sets, such as Coronaviruses, it achieves almost 100% accuracy for classifying its various types. In addition, WalkIm achieves high-speed convergence during network training, as well as reduction of network complexity. Therefore WalkIm method enables us to execute the classifying neural networks on a normal desktop system in a short time interval. Moreover, we addressed the compatibility of WalkIm encoding method with free-space optical processing technology. Taking advantages of optical implementation of convolutional layers, we illustrated that the training time can be reduced by up to 500 time. In addition to all aforementioned advantages, this encoding method preserves the structure of generated images in various modes of sequence transformation, such as reverse complement, complement, and reverse modes. Copyright: © 2022 Akbari Rokn Abadi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
  6. Keywords:
  7. Adoption ; Coronavirinae ; Diagnostic test accuracy study ; Metagenomics ; Nonhuman ; Velocity ; Information processing ; Metagenomics ; Methodology ; Data Collection ; Metagenomics ; Neural Networks, Computer ; Research Design
  8. Source: PLoS ONE ; Volume 17, Issue 4 April , 2022 ; 19326203 (ISSN)
  9. URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267106