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.
Highlights
IntroductionMicroRNAs (miRNAs) are small RNAs that are 20 to 25 nucleotides in length [1], [2]. Line-4 and let-7 are the first two miRNAs discovered in the past two decades [3], [4]
MicroRNAs are small RNAs that are 20 to 25 nucleotides in length [1], [2]
In this paper, we propose a predictive method based on chaos game representation to simultaneously consider linear sequence information and nonlinear relationships
Summary
MicroRNAs (miRNAs) are small RNAs that are 20 to 25 nucleotides in length [1], [2]. Line-4 and let-7 are the first two miRNAs discovered in the past two decades [3], [4]. Many miRNAs have been revealed and identified by using different biological experimental methods, which gives new insights into the functions and regulatory mechanisms of miRNAs. The experiment proves that many miRNAs are expressed in certain types of diseases, including arthritis, adenoid cystic, arteriosclerotic occlusive disease, immune thrombocytopenic purpura, and idiopathic pulmonary hypertension [5]–[10]. For the above reasons, exploring the potential association between miRNA and disease is gradually concerned by scholars [12]–[15]. The high experimental cost, long experimental cycle and sensitivity of noise may hinder the validation of potential miRNA-disease associations through biological experiments. It is necessary to find more effective calculation methods to assist biological experiments to provide effective association candidates to promote the development of biomedicine
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