A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis
A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, mul...
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| Veröffentlicht in: | IEEE/ACM transactions on computational biology and bioinformatics 2012-07, Vol.9 (4), p.1106-1119 |
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| creator | Lazar, C. Taminau, J. Meganck, S. Steenhoff, D. Coletta, A. Molter, C. de Schaetzen, V. Duque, R. Bersini, H. Nowe, A. |
| description | A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them in a unified framework, using standardized notations in order to reveal their technical details and to highlight their common characteristics as well as their particularities. |
| doi_str_mv | 10.1109/TCBB.2012.33 |
| format | Article |
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Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. 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(IEEE) Jul/Aug 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-72d047a0f3f6e3c7fdce42d5c34ed174ead40d451841aadeab5eaea52d3d7b0c3</citedby><cites>FETCH-LOGICAL-c415t-72d047a0f3f6e3c7fdce42d5c34ed174ead40d451841aadeab5eaea52d3d7b0c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6152088$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6152088$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22350210$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lazar, C.</creatorcontrib><creatorcontrib>Taminau, J.</creatorcontrib><creatorcontrib>Meganck, S.</creatorcontrib><creatorcontrib>Steenhoff, D.</creatorcontrib><creatorcontrib>Coletta, A.</creatorcontrib><creatorcontrib>Molter, C.</creatorcontrib><creatorcontrib>de Schaetzen, V.</creatorcontrib><creatorcontrib>Duque, R.</creatorcontrib><creatorcontrib>Bersini, H.</creatorcontrib><creatorcontrib>Nowe, A.</creatorcontrib><title>A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis</title><title>IEEE/ACM transactions on computational biology and bioinformatics</title><addtitle>TCBB</addtitle><addtitle>IEEE/ACM Trans Comput Biol Bioinform</addtitle><description>A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. 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methods</topic><topic>Feature selection</topic><topic>Gene expression</topic><topic>gene expression data</topic><topic>Gene Expression Profiling</topic><topic>gene prioritization</topic><topic>gene ranking</topic><topic>Genetic Markers</topic><topic>information filters</topic><topic>Information Theory</topic><topic>Measurement</topic><topic>Models, Statistical</topic><topic>Oligonucleotide Array Sequence Analysis</topic><topic>ROC Curve</topic><topic>scoring functions</topic><topic>Search methods</topic><topic>Software</topic><topic>statistical methods</topic><topic>Statistics, Nonparametric</topic><topic>Studies</topic><topic>Taxonomy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lazar, C.</creatorcontrib><creatorcontrib>Taminau, J.</creatorcontrib><creatorcontrib>Meganck, S.</creatorcontrib><creatorcontrib>Steenhoff, D.</creatorcontrib><creatorcontrib>Coletta, A.</creatorcontrib><creatorcontrib>Molter, C.</creatorcontrib><creatorcontrib>de Schaetzen, V.</creatorcontrib><creatorcontrib>Duque, R.</creatorcontrib><creatorcontrib>Bersini, H.</creatorcontrib><creatorcontrib>Nowe, A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - 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| subjects | Analysis of Variance Bayes Theorem Bioinformatics biomarker discovery Computational biology Computational Biology - methods Feature selection Gene expression gene expression data Gene Expression Profiling gene prioritization gene ranking Genetic Markers information filters Information Theory Measurement Models, Statistical Oligonucleotide Array Sequence Analysis ROC Curve scoring functions Search methods Software statistical methods Statistics, Nonparametric Studies Taxonomy |
| title | A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis |
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