Titlebook: Computational Intelligence in Bioinformatics; Arpad Kelemen,Ajith Abraham,Yuehui Chen Book 2008 Springer-Verlag Berlin Heidelberg 2008 Ann

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https://doi.org/10.1057/9781137354426mines the production of proteins essential for cellular function. The level of expression of each gene in the genome is modified by controlling whether and how vigorously it is transcribed to RNA, and subsequently translated to protein. RNA and protein expression will influence expression rates of o

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https://doi.org/10.1057/9781137354426pment and drug effect at the molecular level. In this chapter for both exploring and prediction purposes a Time Lagged Recurrent Neural Network with trajectory learning is proposed for identifying and classifying the gene functional patterns from the heterogeneous nonlinear time series microarray ex

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Political and Economic Challenges,ods take a set of estimated covariance model parameters for a non-coding RNA family as given. The difference lies in how the score of a database position with respect to the covariance model is computed. Dynamic programming returns an exact score at the cost of very large computational resource usag

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Case Studies: From Region to Site,se studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine. One important aspect of microarray expression analysis is the classification of the recorded samples which poses many challenges due to the

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