Cited by 51Cancer-related molecular mechanisms researchBioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

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Xiao, Xiaofang, Zhu, Wen, Liao, Bo, Xu, Junlin, Gu, Changlong, Ji, Binbin, Yao, Yuhua, Peng, Lihong +1 more·2018·Frontiers in Genetics

Abstract

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].”

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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
Venue
Frontiers in Genetics
Published
2018-10-16
Volume
9
Pages
411-411
ISSN
1664-8021 (ISSN-L)
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
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

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