1. MISSIM: An Incremental Learning-Based Model With Applications to the Prediction of miRNA-Disease Association
- Author
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Kai Zheng, Zhu-Hong You, Ji-Ren Zhou, Yi-Ran Li, Lei Wang, and Hai-Tao Zeng
- Subjects
Computer science ,0206 medical engineering ,02 engineering and technology ,Machine learning ,computer.software_genre ,Field (computer science) ,Machine Learning ,Correlation ,Neoplasms ,Genetics ,Humans ,Genetic Predisposition to Disease ,Biomedicine ,Hyperparameter ,Biological data ,Forgetting ,business.industry ,Applied Mathematics ,Computational Biology ,Support vector machine ,MicroRNAs ,Artificial intelligence ,Transcriptome ,business ,computer ,Algorithms ,020602 bioinformatics ,Predictive modelling ,Biotechnology - 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.
- Published
- 2021