CLPiDA: A Contrastive Learning Approach for Predicting Potential PiRNA-Disease Associations

Hu, Xiaowen; Zhang, Yuanpeng; Zhang, Jiaxuan; Deng, Lei

doi:10.1109/bibm58861.2023.10385399

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2023

DOI: 10.1109/bibm58861.2023.10385399

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CLPiDA: A Contrastive Learning Approach for Predicting Potential PiRNA-Disease Associations

Xiaowen Hu,

Yuanpeng Zhang,

Jiaxuan Zhang

et al.

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2024

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MRDPDA: A multi‐Laplacian regularized deepFM model for predicting piRNA‐disease associations

Liu,

Zhang,

Ding

et al. 2024

J Cellular Molecular Medi

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PIWI‐interacting RNAs (piRNAs) are a typical class of small non‐coding RNAs, which are essential for gene regulation, genome stability and so on. Accumulating studies have revealed that piRNAs have significant potential as biomarkers and therapeutic targets for a variety of diseases. However current computational methods face the challenge in effectively capturing piRNA‐disease associations (PDAs) from limited data. In this study, we propose a novel method, MRDPDA, for predicting PDAs based on limited data from multiple sources. Specifically, MRDPDA integrates a deep factorization machine (deepFM) model with regularizations derived from multiple yet limited datasets, utilizing separate Laplacians instead of a simple average similarity network. Moreover, a unified objective function to combine embedding loss about similarities is proposed to ensure that the embedding is suitable for the prediction task. In addition, a balanced benchmark dataset based on piRPheno is constructed and a deep autoencoder is applied for creating reliable negative set from the unlabeled dataset. Compared with three latest methods, MRDPDA achieves the best performance on the pirpheno dataset in terms of the five‐fold cross validation test and independent test set, and case studies further demonstrate the effectiveness of MRDPDA.

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MRDPDA: A multi‐Laplacian regularized deepFM model for predicting piRNA‐disease associations

Liu,

Zhang,

Ding

et al. 2024

J Cellular Molecular Medi

View full text Add to dashboard Cite

show abstract

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CLPiDA: A Contrastive Learning Approach for Predicting Potential PiRNA-Disease Associations

Cited by 1 publication

References 38 publications

MRDPDA: A multi‐Laplacian regularized deepFM model for predicting piRNA‐disease associations

MRDPDA: A multi‐Laplacian regularized deepFM model for predicting piRNA‐disease associations

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