IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2562-d869504.html
   My bibliography  Save this article

MultiSec: Multi-Task Deep Learning Improves Secreted Protein Discovery in Human Body Fluids

Author

Listed:
  • Kai He

    (Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Yan Wang

    (Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
    School of Artificial Intelligence, Jilin University, Changchun 130012, China)

  • Xuping Xie

    (Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Dan Shao

    (College of Computer Science and Technology, Changchun University, Changchun 130022, China)

Abstract

Prediction of secreted proteins in human body fluids is essential since secreted proteins hold promise as disease biomarkers. Various approaches have been proposed to predict whether a protein is secreted into a specific fluid by its sequence. However, there may be relationships between different human body fluids when proteins are secreted into these fluids. Current approaches ignore these relationships directly, and therefore their performances are limited. Here, we present MultiSec, an improved approach for secreted protein discovery to exploit relationships between fluids via multi-task learning. Specifically, a sampling-based balance strategy is proposed to solve imbalance problems in all fluids, an effective network is presented to extract features for all fluids, and multi-objective gradient descent is employed to prevent fluids from hurting each other. MultiSec was trained and tested in 17 human body fluids. The comparison benchmarks on the independent testing datasets demonstrate that our approach outperforms other available approaches in all compared fluids.

Suggested Citation

  • Kai He & Yan Wang & Xuping Xie & Dan Shao, 2022. "MultiSec: Multi-Task Deep Learning Improves Secreted Protein Discovery in Human Body Fluids," Mathematics, MDPI, vol. 10(15), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2562-:d:869504
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2562/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/15/2562/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Le-Le Hu & Tao Huang & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Prediction of Body Fluids where Proteins are Secreted into Based on Protein Interaction Network," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
    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. Jianjun He & Hong Gu & Wenqi Liu, 2012. "Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-10, June.
    2. Yu-Fei Gao & Lei Chen & Yu-Dong Cai & Kai-Yan Feng & Tao Huang & Yang Jiang, 2012. "Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.

    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:gam:jmathe:v:10:y:2022:i:15:p:2562-:d:869504. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.