ArticlePLoS computational biology2022
iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Advanced artificial intelligence in piRNA and PIWI-like protein research: A systematic review of recurrent neural networks, long short-term memory, and emerging computational techniquesBiomedica : revista del Instituto Nacional de Salud · 2025Pooled it
- iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations.BMC biology · 2025Article
- Databases and computational methods for the identification of piRNA-related molecules: A survey.Computational and structural biotechnology journal · 2024Review
- MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations.Journal of cellular and molecular medicine · 2024Article
- piRNA-disease association prediction based on multi-channel graph variational autoencoder.PeerJ. Computer science · 2024Article
- iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.PLoS computational biology · 2023Article
- ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network.BMC genomics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Piwi-interacting RNAs (piRNAs) are regarded as drug targets and biomarkers for the diagnosis and therapy of diseases. However, biological experiments cost substantial time and resources, and the existing computational methods only focus on identifying missing associations between known piRNAs and diseases. With the fast development of biological experiments, more and more piRNAs are detected. Therefore, the identification of piRNA-disease associations of newly detected piRNAs has significant theoretical value and practical significance on pathogenesis of diseases. In this study, the iPiDA-LTR predictor is proposed to identify associations between piRNAs and diseases based on Learning to Rank. The iPiDA-LTR predictor not only identifies the missing associations between known piRNAs and diseases, but also detects diseases associated with newly detected piRNAs. Experimental results demonstrate that iPiDA-LTR effectively predicts piRNA-disease associations outperforming the other related methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.