IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0258625.html
   My bibliography  Save this article

An improved deep learning model for hierarchical classification of protein families

Author

Listed:
  • Pahalage Dhanushka Sandaruwan
  • Champi Thusangi Wannige

Abstract

Although genes carry information, proteins are the main role player in providing all the functionalities of a living organism. Massive amounts of different proteins involve in every function that occurs in a cell. These amino acid sequences can be hierarchically classified into a set of families and subfamilies depending on their evolutionary relatedness and similarities in their structure or function. Protein characterization to identify protein structure and function is done accurately using laboratory experiments. With the rapidly increasing huge amount of novel protein sequences, these experiments have become difficult to carry out since they are expensive, time-consuming, and laborious. Therefore, many computational classification methods are introduced to classify proteins and predict their functional properties. With the progress of the performance of the computational techniques, deep learning plays a key role in many areas. Novel deep learning models such as DeepFam, ProtCNN have been presented to classify proteins into their families recently. However, these deep learning models have been used to carry out the non-hierarchical classification of proteins. In this research, we propose a deep learning neural network model named DeepHiFam with high accuracy to classify proteins hierarchically into different levels simultaneously. The model achieved an accuracy of 98.38% for protein family classification and more than 80% accuracy for the classification of protein subfamilies and sub-subfamilies. Further, DeepHiFam performed well in the non-hierarchical classification of protein families and achieved an accuracy of 98.62% and 96.14% for the popular Pfam dataset and COG dataset respectively.

Suggested Citation

  • Pahalage Dhanushka Sandaruwan & Champi Thusangi Wannige, 2021. "An improved deep learning model for hierarchical classification of protein families," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-15, October.
  • Handle: RePEc:plo:pone00:0258625
    DOI: 10.1371/journal.pone.0258625
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0258625
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0258625&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0258625?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Julien Becker & Francis Maes & Louis Wehenkel, 2013. "On the Encoding of Proteins for Disordered Regions Prediction," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    2. Jørgen Hilden, 1991. "The Area under the ROC Curve and Its Competitors," Medical Decision Making, , vol. 11(2), pages 95-101, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arthur De Sá Ferreira & Ney Meziat-Filho & Ana Paula Antunes Ferreira, 2021. "Double threshold receiver operating characteristic plot for three-modal continuous predictors," Computational Statistics, Springer, vol. 36(3), pages 2231-2245, September.
    2. Wilbert B. van den Hout, 2003. "The Area under an ROC Curve with Limited Information," Medical Decision Making, , vol. 23(2), pages 160-166, March.
    3. Bernd Lütkenhöner & Türker Basel, 2013. "Predictive Modeling for Diagnostic Tests with High Specificity, but Low Sensitivity: A Study of the Glycerol Test in Patients with Suspected Menière’s Disease," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-12, November.
    4. J Nevil Amos & Andrew F Bennett & Ralph Mac Nally & Graeme Newell & Alexandra Pavlova & James Q Radford & James R Thomson & Matt White & Paul Sunnucks, 2012. "Predicting Landscape-Genetic Consequences of Habitat Loss, Fragmentation and Mobility for Multiple Species of Woodland Birds," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-12, February.
    5. Zhiheng Wang & Qianqian Yang & Tonghua Li & Peisheng Cong, 2015. "DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    6. V. Robles & C. Bielza & P. Larrañaga & S. González & L. Ohno-Machado, 2008. "Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 345-366, December.
    7. D. J. Hand & C. Anagnostopoulos, 2023. "Notes on the H-measure of classifier performance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 109-124, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0258625. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.