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GCLNSTDA: Predicting tsRNA-Disease Association Based on Contrastive Learning and Negative Sampling

Published: 16 December 2024 Publication History

Abstract

An increasing number of studies have shown that tsRNAs are closely associated with diseases. Using computational methods to predict potential tsRNA-disease associations can effectively reduce the amount of human and material resources consumed. We propose a computational framework (GCLNSTDA) to predict tsRNA-disease associations based on contrastive learning and negative sampling methods. Firstly, we reconstruct the tsRNA-disease association by the truncated singular values. Then, the features of tsRNA and disease are learned by using contrastive learning and graph neural network based on the reconstructed and the original tsRNA-disease association. Finally, the multilayer perceptron is used to calculate the association prediction scores. In addition, we select high-quality negative samples by Bayesian negative sampling method to further improve the model performance. We conduct five-fold cross-validation and denovo experiments on a manually collected tsRNA-disease association dataset, the experimental results show that GCLNSTDA outperforms the other six compared methods. We also perform a case study on lung cancer and the experimental results show that GCLNSTDA is an effective tool for predicting potential associations between tsRNA and disease.

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  1. GCLNSTDA: Predicting tsRNA-Disease Association Based on Contrastive Learning and Negative Sampling

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    cover image ACM Conferences
    BCB '24: Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
    November 2024
    614 pages
    ISBN:9798400713026
    DOI:10.1145/3698587
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 16 December 2024
    Accepted: 24 September 2024
    Revised: 26 August 2024
    Received: 15 July 2024

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    Author Tags

    1. contrastive learning
    2. disease
    3. graph neural networks
    4. negative sampling
    5. tsRNA

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