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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

Results of the growing genetic networks from target genes. Identified network: dashed lines represent the false positives and solid lines the positives. There are no false negatives.
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Figure 9: Results of the growing genetic networks from target genes. Identified network: dashed lines represent the false positives and solid lines the positives. There are no false negatives.

Mentions: The second addressed computational biology problem is genetic network identification. In this case we used an artificial genetic network generated by the approach presented in [20]. The adopted parameters were: 10 nodes, binary quantization, 20 transitions (instants of time), 1 average degree per node and random graphs of Erdös-Rényi. Figure 9 presents the network identification, in which no false negatives occurred and just few false positives. The methodology of network generation is described in Section Network inference, the same as used to generate probabilistic genetic networks of the Plasmodium falciparum from microarray data [15], thus showing the possibility of exploring the software in such an important problem in bioinformatics.


Feature selection environment for genomic applications.

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

Results of the growing genetic networks from target genes. Identified network: dashed lines represent the false positives and solid lines the positives. There are no false negatives.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Results of the growing genetic networks from target genes. Identified network: dashed lines represent the false positives and solid lines the positives. There are no false negatives.
Mentions: The second addressed computational biology problem is genetic network identification. In this case we used an artificial genetic network generated by the approach presented in [20]. The adopted parameters were: 10 nodes, binary quantization, 20 transitions (instants of time), 1 average degree per node and random graphs of Erdös-Rényi. Figure 9 presents the network identification, in which no false negatives occurred and just few false positives. The methodology of network generation is described in Section Network inference, the same as used to generate probabilistic genetic networks of the Plasmodium falciparum from microarray data [15], thus showing the possibility of exploring the software in such an important problem in bioinformatics.

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