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

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Abstract

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.

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Data availability

All raw data is from TCGA. The datasets used and analyzed in the current study are presented in additional supporting files.

Abbreviations

PCGs:

Protein-coding genes

NCGs:

Non-coding genes

PCG-EPs:

Protein-coding gene expression profiles

NCG-EPs:

Non-coding gene expression profiles

CO-EPs:

Co-expression profiles

C-EPs:

Cancer expression profiles

CG:

Comparison group

HFSA:

Hierarchical feature selection algorithm

SAMM:

Single algorithm & multi-model

MASM:

Multi-algorithm & single model

MAMM:

Multi-algorithm & multi-model

MHDMs:

Multiple heterogeneous diagnosis models

MMIECM:

Multi-category multi-state information ensemble-based classification method

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Acknowledgements

XianFang Tang and Zhe Shi contributed equally to this work. This work was supported in part by the National Natural Science Foundation of China under Grant 61773157, and Changsha Key R&D Program under Grant KQ2004011.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61773157, and in part by the Changsha Key Research and Development Program under Grant KQ2004011.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan Province, China

    XianFang Tang, Zhe Shi & Min Jin

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  1. XianFang Tang
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Contributions

XT: Editing and Submission, ZS: Software, Experiment, and Writing. MJ: Conceptualization, Methodology, Experiment Scheme, Writing, and Review.

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Correspondence to Min Jin.

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Tang, X., Shi, Z. & Jin, M. Multi-category multi-state information ensemble-based classification method for precise diagnosis of three cancers. Neural Comput & Applic 33, 15901–15917 (2021). https://doi.org/10.1007/s00521-021-06211-3

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