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The aGFP database contains several gene selection and grouping tools including pre-defined gene families.
Gene Expression Model Selector provides several SVM-based binary or multi-category classification methods and several gene selection methods [ 33].
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The larger the CNV span, the greater the chance that a gene (or several genes) under selection for altered gene dosage will be contained within the duplication or deletion.
Several computational candidate gene selection and prioritization methods have recently been developed.
In the literature, there are several algorithms for gene selection and cancer classification that use a microarray.
The performance of the proposed method was evaluated under several scenarios of gene selection criteria using two well known and challenging datasets: the GCM and NCI60 datasets containing 14 and 9 cancer tumor types, respectively.
The highest value was 82.6%, which was achieved from several combinations of the gene selection method and gene number, including the case of FC-based gene selection and 500 genes.
The gene selection has several advantages [ 3]: 1) keeps or enhances classification accuracy.
We compared the above proposed methods to several recently developed and published gene selection methods: LOOCSFS, GLGS, SVMRFE, SFFS-LS bound, SFS-LS bound, and also T-TEST.
Gene selection is critical in clinical gene expression classification for several reasons.
They found that selection strongly favored several gene variants for tallness in northern and central Europeans, starting 8000 years ago, with a boost coming from the Yamnaya migration, starting 4800 years ago.
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