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Additional resources for Computational Intelligence Techniques for Comparative Genomics: Dedicated to Prof. Allam Appa Rao on the Occasion of His 65th Birthday
Section 2 includes the motivation. Section 3 illustrates the methodology of sparse-modeled ROI. Sections 4 and 5 report experimental observations and conclusions. 2 Motivation Many computer-aided classiﬁcation systems are proposed for classiﬁcation of masses of mammograms . Identiﬁcation of ROI using segmentation is the preliminary phase in the process of classiﬁcation [9, 10]. Feature extraction and classiﬁcation are the lateral steps of mammogram classiﬁcation . Textural feature extraction from ROI using gray level statistical matrices is the key issue in most of these systems .
As this paper is classiﬁed only to S. cerevisiae organism, the future envisaged scope of this work was to introspect AutoTLBO on the other assemblies of molecular functions in the gene ontology biological process. References 1. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer Aided Des 43:303–315. 015 2. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems.
9. The list total, population list, and population total are genes are shown as column 11 in Fig. 9. Figure 10 displays the cluster obtained on yeast4382 with DAVID. 29, which is an optimal value for the dataset of 4,382 gene-IDs. 05. R. Kurada et al. multiple comparison corrections such as Benjamini, Bonferroni, LT, PH, and PT are given in column 8, 9, and 11 of Fig. 10. The upshots of this summative assessment-II are indeed noteworthy. Realistic results are achieved by the usage of bioinformatics tools because the input quality was magniﬁcent.