Download e-book for kindle: Bioinformatics: The Machine Learning Approach (2nd Edition) by Pierre Baldi, Sören Brunak

By Pierre Baldi, Sören Brunak

An unheard of wealth of information is being generated by way of genome sequencing initiatives and different experimental efforts to figure out the constitution and serve as of organic molecules. The calls for and possibilities for reading those information are increasing quickly. Bioinformatics is the improvement and alertness of laptop equipment for administration, research, interpretation, and prediction, in addition to for the layout of experiments. computing device studying methods (e.g., neural networks, hidden Markov types, and trust networks) are perfect for parts the place there's a lot of information yet little thought, that's the location in molecular biology. The aim in computer studying is to extract precious info from a physique of knowledge by means of development reliable probabilistic models--and to automate the method up to attainable. during this booklet Pierre Baldi and Søren Brunak current the major desktop studying methods and observe them to the computational difficulties encountered within the research of organic information. The e-book is aimed either at biologists and biochemists who have to comprehend new data-driven algorithms and at people with a first-rate history in physics, arithmetic, statistics, or machine technological know-how who want to know extra approximately functions in molecular biology. This new moment variation includes elevated insurance of probabilistic graphical types and of the functions of neural networks, in addition to a brand new bankruptcy on microarrays and gene expression. the whole textual content has been broadly revised.

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Additional info for Bioinformatics: The Machine Learning Approach (2nd Edition) (Adaptive Computation and Machine Learning)

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The importance of other nonerror-correcting properties of the genetic code may have been underestimated, and we shall see in chapter 6 that a neural network trained on the mapping between nucleotide triplets and amino acids is simpler for the standard code, and much more complex when trained on more error-correcting genetic codes that have been suggested as potential alternatives to the code found by evolution [524]. The amount of information in biological sequences is related to their com- On the Information Content of Biological Sequences 27 pressibility.

While algorithmic solutions to this problem have been proposed, it may often be better to clean up the data set first and thereby give the underrepresented sequences equal opportunity. It is important to realize that underrepresentation can pose problems both at the primary structure level (sequence redundancy) and at the classification level. Categories of protein secondary structures, for example, are typically skewed, with random coil being much more frequent than beta-sheet. For these reasons, it can be necessary to avoid too closely related sequences in a data set.

Data were taken in part from [390] and references therein (and scaled based on more current estimates); others were compiled from a number of different Internet resources, papers, and books. 1 Gene Content in the Human Genome and other Genomes A variable part of the complete genome sequence in an organism contains genes, a term normally defined as one or several segments that constitute an expressible unit. The word gene was coined in 1909 by the Danish geneticist Wilhelm Johannsen (together with the words genetype and phenotype) long before the physical basis of DNA was understood in any detail.

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Bioinformatics: The Machine Learning Approach (2nd Edition) (Adaptive Computation and Machine Learning) by Pierre Baldi, Sören Brunak

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