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• W1484328231 abstract "Biological knowledge discovery is aimed at quantifying the treatment effects and establishing functional and mechanistic characterization of drug, treatment, or disease [1, 2]. It involves eliciting information at multiple layers of granularity and generalization. It may be identifying gene(s) or proteins, gene–gene interactions, regulatory networks, and biological processes involved in a disease and other biological phenomena. The different levels of granularity are achieved through both supervised and unsupervised analysis frameworks. Sample attributes [3] may be used to identify the genes contributing most and thereby establishing gene–disease associations. The unsupervised framework [4] is used for class discovery involving a multitude of clustering methodologies such as hierarchical/partitional clustering [5–7], biclustering [8], and consensus clustering. Alternatively, it may even be a combination of both supervised and unsupervised methodologies [9]. However, irrespective of the granularity of the hypothesis and the knowledge discovery framework, the necessary requirement is to identify feature sets consisting of genes, proteins, single-nucleotide polymorphisms (SNPs), linkage disequilibrium (LD) blocks, pathways, and interactions involved in the process under study from a huge number of possibilities present in the data. Furthermore, it may be required to analyze the biological implications of the identified genes or interactions based on the multitude of the knowledge available in the literature which too needs selection of a few from a huge number of interactions and pathways, described in the literature." @default.
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• W1484328231 date "2013-12-16" @default.
• W1484328231 modified "2023-10-03" @default.
• W1484328231 title "Statistical Significance Assessment for Biological Feature Selection: Methods and Issues" @default.
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• W1484328231 doi "https://doi.org/10.1002/9781118617151.ch15" @default.
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