Integrating Multiple Heterogeneous Networks for Novel LncRNA-Disease Association Inference

Accumulating experimental evidence has indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes implicated in many human diseases. However, only relatively few experimentally supported lncRNA-disease associations have been reported. Developing ef...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE/ACM transactions on computational biology and bioinformatics Jg. 16; H. 2; S. 396 - 406
Hauptverfasser: Zhang, Jingpu, Zhang, Zuping, Chen, Zhigang, Deng, Lei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Biological activity
Cancer
Cervical cancer
Cervix
Computation
Computational Biology - methods
Computer applications
Computer networks
Diseases
flow propagation
Genetic Association Studies
Glioma
heterogeneous network
Heterogeneous networks
Humans
Identification methods
LncRNA-disease
Neoplasms - genetics
Networks
Ovarian cancer
Predictive models
Protein interaction
Proteins
Reproducibility
Reproducibility of Results
RNA
RNA, Long Noncoding - genetics
Similarity
ISSN:1545-5963, 1557-9964, 1557-9964
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Accumulating experimental evidence has indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes implicated in many human diseases. However, only relatively few experimentally supported lncRNA-disease associations have been reported. Developing effective computational methods to infer lncRNA-disease associations is becoming increasingly important. Current network-based algorithms typically use a network representation to identify novel associations between lncRNAs and diseases. But these methods are concentrated on specific entities of interest (lncRNAs and diseases) and they do not allow to consider networks with more than two types of entities. Considering the limitations in previous computational methods, we develop a new global network-based framework, LncRDNetFlow, to prioritize disease-related lncRNAs. LncRDNetFlow utilizes a flow propagation algorithm to integrate multiple networks based on a variety of biological information including lncRNA similarity, protein-protein interactions, disease similarity, and the associations between them to infer lncRNA-disease associations. We show that LncRDNetFlow performs significantly better than the existing state-of-the-art approaches in cross-validation. To further validate the reproducibility of the performance, we use the proposed method to identify the related lncRNAs for ovarian cancer, glioma, and cervical cancer. The results are encouraging. Many predicted lncRNAs in the top list have been verified by the biological studies.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2017.2701379