Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder Article Swipe
Junjie Shen
,
Huijun Li
,
Xinghao Yu
,
Lu Bai
,
Yongfei Dong
,
Jianping Cao
,
Ke Lü
,
Zaixiang Tang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2022.1091767
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2022.1091767
Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models and evaluated the raw predictive effect of the models. The results indicated that these compressed transformation features could better improve the model’s original predictive performance and might avoid an overfitting problem. This idea may be enlightening for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.
Related Topics
Concepts
Overfitting
Computer science
Lasso (programming language)
Autoencoder
Feature selection
Artificial intelligence
Binary classification
Data mining
Big data
Predictive modelling
Pattern recognition (psychology)
Machine learning
Deep learning
Artificial neural network
Support vector machine
World Wide Web
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2022.1091767
- https://www.frontiersin.org/articles/10.3389/fonc.2022.1091767/pdf
- OA Status
- gold
- Cited By
- 3
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4315433436
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4315433436Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fonc.2022.1091767Digital Object Identifier
- Title
-
Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoderWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-10Full publication date if available
- Authors
-
Junjie Shen, Huijun Li, Xinghao Yu, Lu Bai, Yongfei Dong, Jianping Cao, Ke Lü, Zaixiang TangList of authors in order
- Landing page
-
https://doi.org/10.3389/fonc.2022.1091767Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fonc.2022.1091767/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fonc.2022.1091767/pdfDirect OA link when available
- Concepts
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Overfitting, Computer science, Lasso (programming language), Autoencoder, Feature selection, Artificial intelligence, Binary classification, Data mining, Big data, Predictive modelling, Pattern recognition (psychology), Machine learning, Deep learning, Artificial neural network, Support vector machine, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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