Journal
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
Volume 10, Issue 2, Pages 502-507Publisher
AMER SCIENTIFIC PUBLISHERS
DOI: 10.1166/jmihi.2020.2695
Keywords
Noncoding Ribonucleic Acid; Large-Scale Data; Multitask Learning; TSK Fuzzy Classifier
Funding
- National Natural Science Foundation of China [61702225, 61772241, 61806026]
- Natural Science Foundation of Jiangsu Province [BK20160187, BK20180956]
- 2018 Six Talent Peaks Project of Jiangsu Province [XYDXX-127]
- Science and technology demonstration project of social development of Wuxi [WX18IVJN002]
- Youth Foundation of the Commission of Health and Family Planning of Wuxi [Q201654]
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Recognizing noncoding ribonucleic acid (ncRNA) data is helpful in realizing the regulation of tumor formation and certain aspects of life mechanisms, such as growth, differentiation, development, and immunity. However, the scale of ncRNA data is usually very large. Using machine learning (ML) methods to automatically analyze these data can obtain more precise results than manually analyzing these data, but the traditional ML algorithms can process only small-scale training data. To solve this problem, a novel multitask cross-learning 0-order Takagi-Sugeno-Kang fuzzy classifier (MT-CL-0-TSK-FC) is proposed that uses a multitask cross-learning mechanism to solve the large-scale learning problem of ncRNA data. In addition, the proposed MT-CL-0-TSK-FC method naturally inherits the interpretability of traditional fuzzy systems and eventually generates an interpretable rules-based database to recognize the ncRNA data. The experimental results Indicate that the proposed MT-CL-0-TSK-FC method has a faster running time and better classification accuracy than traditional ML methods.
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