Adaptive boosting-based computational model for predicting potential miRNA-disease associations

Abstract

Motivation Recent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us to solve many problems. Results Due to the high cost of traditional experimental methods, revealing disease-related miRNAs through computational models is a more economical and efficient way. Considering the disadvantages of previous models, in this paper, we developed adaptive boosting for miRNA-disease association prediction (ABMDA) to predict potential associations between diseases and miRNAs. We balanced the positive and negative samples by performing random sampling based on k-means clustering on negative samples, whose process was quick and easy, and our model had higher efficiency and scalability for large datasets than previous methods. As a boosting technology, ABMDA was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights. Here, we used decision tree as our weak classifier. As a result, the area under the curve (AUC) of global and local leave-one-out cross validation reached 0.9170 and 0.8220, respectively. What is more, the mean and the standard deviation of AUCs achieved 0.9023 and 0.0016, respectively in 5-fold cross validation. Besides, in the case studies of three important human cancers, 49, 50 and 50 out of the top 50 predicted miRNAs for colon neoplasms, hepatocellular carcinoma and breast neoplasms were confirmed by the databases and experimental literatures. Availability and implementation The code and dataset of ABMDA are freely available at https://github.com/githubcode007/ABMDA. Supplementary information Supplementary data are available at Bioinformatics online.

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

“…Fourth, the association score between the miRNA and disease was computed using final projection matrix and feature profiles from miRNA and disease perspective respectively, and then the average of these two scores was the final prediction result. In addition, Zhao et al (2019) further proposed the model named Adaptive Boosting for MiRNA-Disease Association prediction (ABMDA). In order to balance positive samples and negative samples, all unknown samples were divided into k clusters with k-means clustering and the same amount of negative samples were randomly selected from each cluster, and the number of total negative samples was almost equal to the positive.…”

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

“…Interestingly, they extracted features and constructed objective functions from miRNA and disease perspectives, separately. Chen et al [42] used a decision tree as a weak classifier and then integrated these weak classifiers into a strong classifier according to weights. It is worth noting that they implemented k-means to balance positive samples and negative samples.…”

MSFSP: A Novel miRNA–Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

“…Inspired by the successful application of machine learning methods in the field of bioinformatics, many researchers used supervised machine learning methods to predict a miRNAdisease association (Chen et al, 2015a(Chen et al, ,b, 2017a(Chen et al, , 2018d(Chen et al, ,f, 2019aLuo et al, 2017a;Xuan et al, 2018Xuan et al, , 2019bWang C.-C. et al, 2019;Wang L. et al, 2019;Zhang L. et al, 2019;Zhao et al, 2019), but which need negative samples for training. Because it is hard to obtain the experimentally verified less-known miRNA-disease associations and negative samples, some semi-supervised learning approaches (such as regularized least squares) with remarkable prediction results were proposed (Chen and Huang, 2017;Chen et al, 2017cPeng et al, 2017b;Xu et al, 2019).…”