BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network
Open accessAbstract
In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers.
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Several researches proved that coding genes, noncoding RNAs, and proteins are related to diseases, including AD.
“Several researches proved that coding genes/noncoding RNAs/proteins were related to diseases, including AD [29,30,31,32,33,34,35,36].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [35]
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- Authors
- Xiao, X., Zhu, W., Liao, B., Xu, J., Gu, C., Ji, B., Yao, Y., Peng, L., & Yang, J.
- Affiliations
- Hunan University (CN)
- Hainan Normal University (CN)
- Hunan University of Technology (CN)
- Corresponding author
- Liao, Bo
- Yang, Jialiang
- ORCID
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2018-10-16
- Volume
- 9
- Pages
- 411-411
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC6232683
- OpenAlex
- W2897174088
- Cited by
- 47
- References
- 83
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 5 · 2022: 12 · 2021: 9 · 2020: 13 · 2019: 5 · 2018: 1
- FWCI
- 2.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61702054, 61300128, 11171369, 61672214, 61272395, 61572178, 61472127, 61873076, 61863010, 61370171)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- lncRNA-disease association prediction, long non-coding RNA, heterogeneous networks, disease semantic similarity, cervical cancer, glioma, non-small cell lung cancer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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