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Feature selection environment for genomic applications.

Lopes FM, Martins DC, Cesar RM - BMC Bioinformatics (2008)

Bottom Line: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction).Our experiments have shown the software effectiveness in two distinct types of biological problems.Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

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Affiliation: Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil. fabricio@utfpr.edu.br

ABSTRACT

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e.g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application.

Results: The intent of this work is to provide an open-source multiplatform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes (targets or predictors) is also implemented in the system.

Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

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Related in: MedlinePlus

Flowchart of the SFFS algorithm. Simplified flowchart of the SFFS algorithm (adapted from [21]).
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Figure 1: Flowchart of the SFFS algorithm. Simplified flowchart of the SFFS algorithm (adapted from [21]).

Mentions: In order to circumvent the nesting effect, the Sequential Floating Forward Selection (SFFS) is also available in the software. The SFFS algorithm may be formalized as in [5]. A schematic flowchart of the SFFS algorithm is presented in Figure 1. In this algorithm, the SFS and the SBS (Sequential Backward Search – a counterpart of the SFS) are successively applied.


Feature selection environment for genomic applications.

Lopes FM, Martins DC, Cesar RM - BMC Bioinformatics (2008)

Flowchart of the SFFS algorithm. Simplified flowchart of the SFFS algorithm (adapted from [21]).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2655091&req=5

Figure 1: Flowchart of the SFFS algorithm. Simplified flowchart of the SFFS algorithm (adapted from [21]).
Mentions: In order to circumvent the nesting effect, the Sequential Floating Forward Selection (SFFS) is also available in the software. The SFFS algorithm may be formalized as in [5]. A schematic flowchart of the SFFS algorithm is presented in Figure 1. In this algorithm, the SFS and the SBS (Sequential Backward Search – a counterpart of the SFS) are successively applied.

Bottom Line: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction).Our experiments have shown the software effectiveness in two distinct types of biological problems.Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil. fabricio@utfpr.edu.br

ABSTRACT

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e.g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application.

Results: The intent of this work is to provide an open-source multiplatform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes (targets or predictors) is also implemented in the system.

Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

Show MeSH
Related in: MedlinePlus