Paper Info
Title
CGMDA: An Approach to Predict and Validate MicroRNA-Disease Associations by Utilizing Chaos Game Representation and LightGBM
Abstract
Recent studies have shown that microRNAs (miRNAs) play an important role in complex human diseases. Identifying potential miRNA-disease associations is useful for understanding the pathogenesis. However, there are currently only a few methods proposed to predict miRNA-disease association based on sequence information. And these methods can only quantify nonlinear sequence relationships without taking linear sequence information into account. In this work, we designed a computational method for predicting miRNA-disease association based on chaos game representation, called CGMDA, to overcome these problems. CGMDA combines association information with miRNA sequence information, miRNA functional information and disease semantic information to improve prediction accuracy. In particular, we use chaos game representation (CGR) technology for the first time to transform miRNA sequence information into image information and extract its features. In the cross-validation experiment, CGMDA achieved a mean the area under the receiver operating characteristic curve (AUC) of 0.9099 on the HMDD v3.0 data set. To better evaluate the performance of CGMDA, we compared it to different classifiers and related prediction methods. In addition, CGMDA is applied to three human complex diseases. The results showed that of the top 40 disease-related miRNAs predicted, 39 (Breast Neoplasm), 39 (Lymphoma) and 38 (Colon Neoplasm) were validated by experiments in case studies. These experimental results show that CGMDA is a reliable tool and has potential application prospects in assisting early diagnosis and treatment of prognosis.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2940470
IEEE ACCESS
Keywords
DocType
Volume
miRNAs,chaos game representation,disease,heterogenous information,LightGBM
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Kai Zheng1164.39
Lei Wang2254.58
Zhuhong You374855.20