Download Bioinformatics - A Concept-Based Introduction by Venkatarajan Mathura, Pandjassarame Kangueane PDF

By Venkatarajan Mathura, Pandjassarame Kangueane

ISBN-10: 1441946586

ISBN-13: 9781441946584

Bioinformatics is an evolving box that's becoming more popular as a result of genomics, proteomics and different high-throughput organic equipment. The functionality of bioinformatic scientists contains organic facts garage, retrieval and in silico research of the implications from large-scale experiments. This calls for a snatch of data mining algorithms, an intensive figuring out of organic wisdom base, and the logical dating of entities that describe a strategy or the method. Bioinformatics researchers are required to gain knowledge of in multidisciplinary fields of biology, arithmetic and machine technological know-how. at present the necessities are chuffed via advert hoc researchers who've particular talents in biology or mathematics/computer technological know-how. however the studying curve is steep and the time required to speak utilizing area particular phrases is turning into a huge bottle neck in medical productiveness. This workbook offers hands-on event which has been missing for certified bioinformatics researchers.

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Extra resources for Bioinformatics - A Concept-Based Introduction

Sample text

J. (2005). PromoterPlot: a graphical display of promoter similarities by pattern recognition, Nucleic Acids Res 33, W423–6. Eddy, S. R. (2004). What is Bayesian statistics? Nat Biotechnol 22, 1177–8. Giddings, M. , Shah, A. , Atkins, J. , Gesteland, R. , and Matveeva, O. V. (2002). Artificial neural network prediction of antisense oligodeoxynucleotide activity, Nucleic Acids Res 30, 4295–304. , Walker, J. , Wong, W. , Davis, K. , Buxbaum, J. , and Fienberg, A. A. (2001). Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia, Proc Natl Acad Sci U S A 98, 4746–51.

01 means that the chance of observing sample data is only 1/100 while there is no effect or null is true (no effect on treatment). Since the chance of such observation is very small, we reject null hypothesis and conclude that there is a difference between control and the treatment group. Several statistics exist: t-statistic is for testing mean, F, or Chi-test for testing variance. Hypothesis testing is used in micro array data analysis, sequence analysis, etc. 3 Decision Tree Decision trees are a simple approach to the problem of learning from a set of independent instances.

Mathura, V. , Schein, C. , and Braun, W. (2003). Identifying property based sequence motifs in protein families and superfamilies: application to DNase-1 related endonucleases, Bioinformatics 19, 1381–90. Oldfield, T. (2002). Pattern-recognition methods to identify secondary structure within X-ray crystallographic electron-density maps, Acta Crystallogr D Biol Crystallogr 58, 487–93. , and Sturmfels, B. (2004). Parametric inference for biological sequence analysis, Proc Natl Acad Sci U S A 101, 16138–43.

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Bioinformatics - A Concept-Based Introduction by Venkatarajan Mathura, Pandjassarame Kangueane

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