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Nowadays, technology and technological diversity of the gene expression measurements raise new issues related to proper data analysis and accurate interpretation of results. Because gene expression measurement includes many complex steps and is performed on randomly selected cells from population, the measured value of gene expression is a stochastic variable, and may vary in wide range. Consequently,...
Gene signatures have been utilized to discover the inferred biological functions under gene expression profiles. We present a novel sample scoring method called Signature-score (S-score). S-score quantifies the expression pattern by using gene signatures and has better accuracy and robustness than other scoring methods. A confidence boundary of S-score is determined to identify the status of samples...
High dimensionality is one of the major problems in data mining, occurring when there is a large abundance of attributes. One common technique used to alleviate high dimensionality is feature selection, the process of selecting the most relevant attributes and removing irrelevant and redundant ones. Much research has been done towards evaluating the performance of classifiers before and after feature...
Machine Learning algorithms have been widely used for gene expression data classification, despite the fact that these data have often intrinsic limitations, such as high dimensionality and a small number of examples. Few studies try to characterize to which extent these aspects can influence the performance of the classification models induced. In this paper we compute different measures characterizing...
We aim at finding the smallest set of genes that can ensure highly accurate classification of cancers from microarray data by using supervised machine learning algorithms. The significance of finding the minimum gene subsets is three-fold: 1) it greatly reduces the computational burden and "noise" arising from irrelevant genes. In the examples studied in this paper, finding the minimum gene...
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