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Research article
Jiang Jiang
National University of Defense Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2483-2947
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Numerous pieces of clinical evidence have shown that many phenotypic traits of human disease are related to their gut microbiome. Through supervised classification, it is feasible to determine the human disease states by revealing the intestinal microbiota compositional information. However, the abundance matrix of microbiome data is so sparse, an interpretable deep model is crucial to further represent and mine the data for expansion, such as the deep forest. What's more, overfitting can still exist in the original deep forest model when dealing with such “large p, small n” biology data. Feature reduction is considered to improve the ensemble forest model especially towards the disease identification in the human microbiota. In this work, we propose the kernel principal components based cascade forest method, so-called KPCCF, to classify the disease states of patients by using taxonomic profiles of the microbiome at the family level. In detail, the kernel principal components analysis method is first used to reduce the original dimension of human microbiota datasets. Besides, the processed data is fed into the cascade forest to preliminarily discriminate the disease state of the samples. Thus, the proposed KPCCF algorithm can represent the small-scale and high-dimension human microbiota datasets with the sparse feature matrix. Systematic comparison experiments demonstrate that our method consistently outperforms the state-of-the-art methods with the comparative study on 4 datasets. Additionally, compared to other dimensionality reduction methods, the kernel principal components analysis method is more suitable for microbiota datasets.
Keywords
Human microbiota, Supervised classification, Kernel principal components, Cascade forest, Disease identification
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CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
Version 2
Posted 18 Sep, 2020
No community comments so far
Reviewer #2 agreed
On 27 Oct, 2020
Review #2 received
Received 27 Oct, 2020
Review #1 received
Received 17 Oct, 2020
Reviewers invited
Invitations sent on 22 Sep, 2020
Reviewer #1 agreed
On 22 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 16 Sep, 2020
Editor invited
On 16 Sep, 2020
No comments provided
Editorial decision: Major revision
On 18 Aug, 2020
Review #2 received
Received 15 Jul, 2020
Reviewer #2 agreed
On 23 Jun, 2020
Reviewers invited
Invitations sent on 04 Jun, 2020
Reviewer #1 agreed
On 04 Jun, 2020
Review #1 received
Received 04 Jun, 2020
Editor assigned
On 12 May, 2020
Submission checks complete
On 11 May, 2020
Editor invited
On 11 May, 2020
First submitted
On 08 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
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Subject Areas
Medical Informatics
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A new preprint design is coming!
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See the published version of this preprint at BMC Medical Informatics and Decision Making.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Jiang Jiang
National University of Defense Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2483-2947
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
Version 2
Posted 18 Sep, 2020
No community comments so far
Reviewer #2 agreed
On 27 Oct, 2020
Review #2 received
Received 27 Oct, 2020
Review #1 received
Received 17 Oct, 2020
Reviewers invited
Invitations sent on 22 Sep, 2020
Reviewer #1 agreed
On 22 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 16 Sep, 2020
Editor invited
On 16 Sep, 2020
No comments provided
Editorial decision: Major revision
On 18 Aug, 2020
Review #2 received
Received 15 Jul, 2020
Reviewer #2 agreed
On 23 Jun, 2020
Reviewers invited
Invitations sent on 04 Jun, 2020
Reviewer #1 agreed
On 04 Jun, 2020
Review #1 received
Received 04 Jun, 2020
Editor assigned
On 12 May, 2020
Submission checks complete
On 11 May, 2020
Editor invited
On 11 May, 2020
First submitted
On 08 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Informatics and Decision Making.
Numerous pieces of clinical evidence have shown that many phenotypic traits of human disease are related to their gut microbiome. Through supervised classification, it is feasible to determine the human disease states by revealing the intestinal microbiota compositional information. However, the abundance matrix of microbiome data is so sparse, an interpretable deep model is crucial to further represent and mine the data for expansion, such as the deep forest. What's more, overfitting can still exist in the original deep forest model when dealing with such “large p, small n” biology data. Feature reduction is considered to improve the ensemble forest model especially towards the disease identification in the human microbiota. In this work, we propose the kernel principal components based cascade forest method, so-called KPCCF, to classify the disease states of patients by using taxonomic profiles of the microbiome at the family level. In detail, the kernel principal components analysis method is first used to reduce the original dimension of human microbiota datasets. Besides, the processed data is fed into the cascade forest to preliminarily discriminate the disease state of the samples. Thus, the proposed KPCCF algorithm can represent the small-scale and high-dimension human microbiota datasets with the sparse feature matrix. Systematic comparison experiments demonstrate that our method consistently outperforms the state-of-the-art methods with the comparative study on 4 datasets. Additionally, compared to other dimensionality reduction methods, the kernel principal components analysis method is more suitable for microbiota datasets.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
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