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Review

Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data

1
Department of Information Systems, Zefat Academic College, Zefat 13206, Israel
2
Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat 13206, Israel
3
Institute of Bioinformatics, International Technology Park, Bangalore 560066, India
4
Manipal Academy of Higher Education (MAHE), Manipal 576104, India
5
Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri 38080, Turkey
*
Author to whom correspondence should be addressed.
Entropy 2021, 23(1), 2; https://doi.org/10.3390/e23010002
Received: 27 November 2020 / Revised: 14 December 2020 / Accepted: 16 December 2020 / Published: 22 December 2020
(This article belongs to the Special Issue Statistical Inference from High Dimensional Data)
In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. One of the main goals of this review is to explore the existing methods that integrate different types of information in order to improve the identification of the biomolecular signatures of diseases and the discovery of new potential targets for treatment. These integrative approaches are expected to aid the prediction, diagnosis, and treatment of diseases, as well as to enlighten us on disease state dynamics, mechanisms of their onset and progression. The integration of various types of biological information will necessitate the development of novel techniques for integration and data analysis. Another aim of this review is to boost the bioinformatics community to develop new approaches for searching and determining significant groups/clusters of features based on one or more biological grouping functions. View Full-Text
Keywords: feature selection; feature ranking; grouping; clustering; biological knowledge feature selection; feature ranking; grouping; clustering; biological knowledge
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MDPI and ACS Style

Yousef, M.; Kumar, A.; Bakir-Gungor, B. Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data. Entropy 2021, 23, 2. https://doi.org/10.3390/e23010002

AMA Style

Yousef M, Kumar A, Bakir-Gungor B. Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data. Entropy. 2021; 23(1):2. https://doi.org/10.3390/e23010002

Chicago/Turabian Style

Yousef, Malik, Abhishek Kumar, and Burcu Bakir-Gungor. 2021. "Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data" Entropy 23, no. 1: 2. https://doi.org/10.3390/e23010002

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