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Research article
Kaveh Kavousi
Institute of biochemistry and biophysics (IBB)
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1906-3912
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance.
Methods
This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study was to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies regarding EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures.
Results
By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values.
Conclusions
To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.
Keywords
EC machine learning; Cancer; Prognosis and diagnosis; Data integration; Decision integration; Multi-omics integration
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
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.
Research article
Kaveh Kavousi
Institute of biochemistry and biophysics (IBB)
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1906-3912
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 03 Aug, 2019
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance.
Methods
This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study was to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies regarding EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures.
Results
By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values.
Conclusions
To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
This is a list of supplementary files associated with this preprint. Click to download.
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