Next Article in Journal
Analysis of Catecholamines and Pterins in Inborn Errors of Monoamine Neurotransmitter Metabolism—From Past to Future
Next Article in Special Issue
Long Non-Coding RNA in the Pathogenesis of Cancers
Previous Article in Journal
Calcitriol Attenuates Doxorubicin-Induced Cardiac Dysfunction and Inhibits Endothelial-to-Mesenchymal Transition in Mice
Previous Article in Special Issue
Oncogenic Role of ZFAS1 lncRNA in Head and Neck Squamous Cell Carcinomas
Open AccessArticle

Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA–miRNA–Disease–Drug–Protein Graph

by Zhen-Hao Guo 1,2,†, Hai-Cheng Yi 1,2,† and Zhu-Hong You 1,2,*,†
The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
University of Chinese Academy of Sciences, Beijing 100049, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Cells 2019, 8(8), 866;
Received: 5 June 2019 / Revised: 20 July 2019 / Accepted: 31 July 2019 / Published: 9 August 2019
(This article belongs to the Special Issue lncRNA and Cancer)
One key issue in the post-genomic era is how to systematically describe the associations between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens the possibility of investigating the relationships between different molecules from a global perspective. In this article, a molecular association network (MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attribute of the node itself (e.g., sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the behavior of the node in the complex network (associations with other nodes). A random forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieved a superb performance with an area under curve (AUC) of 0.9735 under a five-fold cross-validation, which showed that the proposed method could provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment. View Full-Text
Keywords: network biology; LINE; lncRNA; protein; miRNA; drug; disease network biology; LINE; lncRNA; protein; miRNA; drug; disease
Show Figures

Graphical abstract

MDPI and ACS Style

Guo, Z.-H.; Yi, H.-C.; You, Z.-H. Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA–miRNA–Disease–Drug–Protein Graph. Cells 2019, 8, 866.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop