A comprehensive survey on computational learning methods for analysis of gene expression data
Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2022.907150/full |
| _version_ | 1811220449182351360 |
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| author | Nikita Bhandari Rahee Walambe Rahee Walambe Ketan Kotecha Ketan Kotecha Satyajeet P. Khare |
| author_facet | Nikita Bhandari Rahee Walambe Rahee Walambe Ketan Kotecha Ketan Kotecha Satyajeet P. Khare |
| author_sort | Nikita Bhandari |
| collection | DOAJ |
| description | Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome. |
| first_indexed | 2024-04-12T07:42:05Z |
| format | Article |
| id | doaj.art-50c00ae8435e4cdf88f566516c3071e4 |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-04-12T07:42:05Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-50c00ae8435e4cdf88f566516c3071e42022-12-22T03:41:47ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2022-11-01910.3389/fmolb.2022.907150907150A comprehensive survey on computational learning methods for analysis of gene expression dataNikita Bhandari0Rahee Walambe1Rahee Walambe2Ketan Kotecha3Ketan Kotecha4Satyajeet P. Khare5Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaElectronics and Telecommunication Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, IndiaComputer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, IndiaSymbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, IndiaComputational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.https://www.frontiersin.org/articles/10.3389/fmolb.2022.907150/fullgene expressionmicroarraymachine learningdeep learningmissing value imputationfeature selection |
| spellingShingle | Nikita Bhandari Rahee Walambe Rahee Walambe Ketan Kotecha Ketan Kotecha Satyajeet P. Khare A comprehensive survey on computational learning methods for analysis of gene expression data Frontiers in Molecular Biosciences gene expression microarray machine learning deep learning missing value imputation feature selection |
| title | A comprehensive survey on computational learning methods for analysis of gene expression data |
| title_full | A comprehensive survey on computational learning methods for analysis of gene expression data |
| title_fullStr | A comprehensive survey on computational learning methods for analysis of gene expression data |
| title_full_unstemmed | A comprehensive survey on computational learning methods for analysis of gene expression data |
| title_short | A comprehensive survey on computational learning methods for analysis of gene expression data |
| title_sort | comprehensive survey on computational learning methods for analysis of gene expression data |
| topic | gene expression microarray machine learning deep learning missing value imputation feature selection |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2022.907150/full |
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