Multi-category multi-state information ensemble-based classification method for precise diagnosis of three cancers

Although cancer diagnosis research has continuously made breakthroughs in a single indicator, it is a challenging task to improve its multiple joint indicators. This study proposes a multi-category multi-state information ensemble-based classification method. We fuse protein-coding and non-coding ge...

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Bibliographic Details
Published in:Neural computing & applications 2021-11, Vol.33 (22), p.15901-15917
Main Authors: Tang, XianFang, Shi, Zhe, Jin, Min
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Cancer
Classification
Coding
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Diagnosis
Gene expression
Image Processing and Computer Vision
Indicators
Medical diagnosis
Original Article
Probability and Statistics in Computer Science
Sensitivity
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Summary:Although cancer diagnosis research has continuously made breakthroughs in a single indicator, it is a challenging task to improve its multiple joint indicators. This study proposes a multi-category multi-state information ensemble-based classification method. We fuse protein-coding and non-coding genes to construct co-expression profiles, which ensemble the field information of classical genetics and epigenetics. A hierarchical feature selection algorithm based on control groups is put forward to quickly remove irrelevant and redundant features without the bias caused by unbalanced dataset. Multiple heterogeneous diagnosis models, which ensemble multiple diagnosis model structures and model states, are constructed and a competition mechanism is then introduced to automatically select the best model from multiple heterogeneous models without deeply grasping the positive and negative fusion effects between different algorithms and features. We apply the proposed method to classify three high-incidence cancers, in which the classification accuracy and sensitivity are over 99.23% and the classification specificity is over 97.37%. This illustrates that the proposed method has upgraded the three joint indicators of cancer diagnosis at the same time. Compared with the state-of-the-art classification methods, the classification accuracy has been improved by 2.23–9.23%, the sensitivity by 6.25–37.40%, and the specificity by 0–12.02%. In addition, feature analysis reveals three biological findings.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-06211-3