Integrated Theory-and Data-Driven Feature Selection in Gene Expression Data Analysis

Raghu, Vineet K.; Ge, Xiaoyu; Chrysanthis, Panos K.; Benos, Panayiotis V.

doi:10.1109/icde.2017.223

Cited by 25 publications

(14 citation statements)

References 30 publications

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“…Alternatively, in [41], Altinigneli et al present a parallelized form of the LogicFS algorithm applied on stimulated datasets and real schizophrenia datasets for predicting SNP interactions and shows a great running time improvement compared to non-parallelized LogicFS. On the other hand, in [42], Raghu et al present an integrative feature selection method for finding a maximally relevant and diverse gene sets with preferential diversity using an importance score that combines both prior knowledge and data inherent information. On the strength of itegrative feature selection methods, AlFarraj et al [43] examine the Ant Colony Optimization based feature selection process.…”

Section: Itegrative Methodsmentioning

confidence: 99%

Feature selection methods and genomic big data: a systematic review

et al. 2019

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With the advance of computational techniques, the amount of genomic data has risen exponentially, with a rapid rate [1] making it hard to utilize such data in the medical field without appropriate pre-processing, which in turn leads to more complexity and veracity issues [2] eventually creating multiple complications such as storage, analysis, privacy and security. Therefore, genomic data may look easy to handle in terms of its volume, but it actually requires quite a complicated process due to the complexity, heterogeneity and hybridity of its features. This process is entitled knowledge discovery process [3]: • Data recording Includes the different challenges and tools regarding the capture and storage of data. • Data pre-processing Which includes all the operations of cleaning and appropriation of the captured data to the ready to analyze form in order to optimize the analysis step. • Data analysis The task of evaluating data using different algorithms following a logical reasoning to examine each component of the data provided, with the aim of dispensing insightful outcomes.

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Section: Itegrative Methodsmentioning

confidence: 99%

Feature selection methods and genomic big data: a systematic review

et al. 2019

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“…The integrative gene selection approach that is proposed by Raghu et al [ 23 ] makes use of KEGG, DisGeNET, and further genetic meta information [ 20 ]. In their approach, for each gene, (i) the importance score and (ii) the gene distance metrics are computed.…”

Section: Gene Selection Approaches For Gene Expression Datasetsmentioning

confidence: 99%

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

Yousef

Kumar

Bakır-Güngör

2020

Entropy

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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.

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“…The integrative gene selection approach that is proposed by Raghu et al [23] makes use of KEGG, DisGeNET, and further genetic meta information [20]. In their approach, for each gene, i) the importance score and ii) the gene distance metrics are computed.…”

Section: Integrative Gene Selectionmentioning

confidence: 99%

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

Yousef¹,

Kumar²,

Bakır-Güngör³

2020

Preprint

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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. For gene expression data analysis, most of the existing feature selection methods rely on expression values alone to select the genes; and biological knowledge is integrated at the end of the analysis in order to gain biological insights or to support the initial findings. 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. Since the integrative approach attracted attention in the gene expression domain, lately the gene selection process shifted from being purely data-centric to more incorporative analysis with additional biological knowledge.

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Integrated Theory-and Data-Driven Feature Selection in Gene Expression Data Analysis

Cited by 25 publications

References 30 publications

Feature selection methods and genomic big data: a systematic review

Feature selection methods and genomic big data: a systematic review

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

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

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