Abstract
In the past few years, the prediction models have shown remarkable performance in most biological correlation prediction tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. These models often encounter training issues such as sensitivity to hyperparameter tuning and "catastrophic forgetting" when adding new data. However, with the development of biomedicine and the accumulation of biological data, new predictive models are required to face the challenge of adapting to change. To this end, we propose a computational approach based on Broad learning system (BLS) to predict potential disease-associated miRNAs that retain the ability to distinguish prior training associations when new data need to be adapted. In particular, we are introducing incremental learning to the field of biological association prediction for the first time and proposed a new method for quantifying sequence similarity. In the performance evaluation, the AUC in the 5-fold cross-validation was 0.9400 +/- 0.0041. To better assess the effectiveness of MISSIM, we compared it with various classifiers and former prediction models. Its performance is superior to the previous method. Besides, the case study on identifying miRNAs associated with breast neoplasms, lung neoplasms and esophageal neoplasms show that 34, 36 and 35 out of the top 40 associations predicted by MISSIM are confirmed by recent biomedical resources. These results provide ample convincing evidence of this approach have potential value and prospect in promoting biomedical research productivity.
Highlights
MICRORNAS regulate gene expression in some physiological processes, such as apoptosis and differentiation of cells, through complementary base pairing with messenger RNA [1], [2], [3]
MISSIM is superior to other methods, indicating that the similarity of sequence information based on chaos game and efficient incremental learning by lateral expansion can improve the prediction performance of miRNA-disease association
We propose a model based on incremental learning to predict miRNA-disease associations, called MISSIM
Summary
MICRORNAS (miRNA) regulate gene expression in some physiological processes, such as apoptosis and differentiation of cells, through complementary base pairing with messenger RNA (mRNA) [1], [2], [3]. In the past five years, traditional prediction models have been proposed to solve biological problems [11], [12], [13], [14], [15], [16], [17], [18], [19] They are based primarily on similarity or on machine learning [20]. MISSIM to solve the problem of learning such incremental available data in biological association prediction. Another innovation of the proposed method is to propose an algorithm for quantifying sequence similarity.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE/ACM transactions on computational biology and bioinformatics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.