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Adap-BDCM: Adaptive Bilinear Dynamic Cascade Model for Classification Tasks on CNV Datasets

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Abstract

Copy number variation (CNV) is an essential genetic driving factor of cancer formation and progression, making intelligent classification based on CNV feasible. However, there are a few challenges in the current machine learning and deep learning methods, such as the design of base classifier combination schemes in ensemble methods and the selection of layers of neural networks, which often result in low accuracy. Therefore, an adaptive bilinear dynamic cascade model (Adap-BDCM) is developed to further enhance the accuracy and applicability of these methods for intelligent classification on CNV datasets. In this model, a feature selection module is introduced to mitigate the interference of redundant information, and a bilinear model based on the gated attention mechanism is proposed to extract more beneficial deep fusion features. Furthermore, an adaptive base classifier selection scheme is designed to overcome the difficulty of manually designing base classifier combinations and enhance the applicability of the model. Lastly, a novel feature fusion scheme with an attribute recall submodule is constructed, effectively avoiding getting stuck in local solutions and missing some valuable information. Numerous experiments have demonstrated that our Adap-BDCM model exhibits optimal performance in cancer classification, stage prediction, and recurrence on CNV datasets. This study can assist physicians in making diagnoses faster and better.

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Code Availability

The source codes for the Adap-BDCM model and the CNV dataset for cancer classification have been archived in the GitHub repository (https://github.com/junhonga/Adap-BDCM).

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Acknowledgements

In this paper, the work is supported by the Fundamental Research Program of Shanxi Province (General program) (Grant No. 202303021211082, 202303021211025).

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Authors and Affiliations

  1. College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030600, China

    Liancheng Jiang, Yizhen Wang, Yongfei Wu & Junhong Yue

  2. College of Computer Science and Technology, Taiyuan Normal University, Taiyuan, 030619, China

    Liye Jia

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Contributions

Concept and design: Liancheng Jiang and Liye Jia, data collection and analysis: Yizhen Wang; drafting of the article: Liancheng Jiang, Liye Jia and Junhong Yue; critical revision of the article for important content: Liancheng Jiang, Liye Jia, Yizhen Wang, Yongfei Wu, Junhong Yue, All the authors approved the final article.

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Correspondence to Junhong Yue.

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Jiang, L., Jia, L., Wang, Y. et al. Adap-BDCM: Adaptive Bilinear Dynamic Cascade Model for Classification Tasks on CNV Datasets. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00635-w

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