File Entry: A Hybrid of SVM and SCAD with Group-specific Tuning Parameter for Pathway-based Microarray Analysis. Advances in Intelligent and Soft-Computing. (Appear in press)
Created: 2012-05-11 01:39:36
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| Title | A Hybrid of SVM and SCAD with Group-specific Tuning Parameter for Pathway-based Microarray Analysis. Advances in Intelligent and Soft-Computing. (Appear in press) |
| File name | M.F._Misman2012-A_Hybrid_of_SVM_and_SCAD_with_Group-specific_Tuning_Parameter_for_Pathway-based_Microarray_Analysis._Adv.pdf |
| File size | 305931 |
| SHA1 | 7eda34c4db488a64ab2c629bc73b12e48a236065 |
| Content type | Adobe PDF |
Description
The incorporation of pathway data into the microarray analysis had lead
to a new era in advance understanding of biological processes. However, this advancement
is limited by the two issues in quality of pathway data. First, the pathway
data are usually made from the biological context free, when it comes to a
specific cellular process (e.g. lung cancer development), it can be that only several
genes within pathways are responsible for the corresponding cellular process.
Second, pathway data commonly curated from the literatures, it can be that some
pathway may be included with the uninformative genes while the informative
genes may be excluded. In this paper, we proposed a hybrid of support vector machine
and smoothly clipped absolute deviation with group-specific tuning parameters
(gSVM-SCAD) to select informative genes within pathways before the pathway
evaluation process. Our experiments on lung cancer and gender data sets
show that gSVM-SCAD obtains significant results in classification accuracy and
in selecting the informative genes and pathways.
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