Read e-book online Feature Selection and Ensemble Methods for Bioinformatics: PDF

By Oleg Okun

ISBN-10: 1609605578

ISBN-13: 9781609605575

ISBN-10: 1609605586

ISBN-13: 9781609605582

Laptop studying is the department of synthetic intelligence whose target is to advance algorithms that upload studying functions to desktops. Ensembles are a vital part of laptop studying. a customary ensemble contains a number of algorithms appearing the duty of prediction of the category label or the measure of sophistication club for a given enter offered as a collection of measurable features, referred to as good points. characteristic choice and Ensemble tools for Bioinformatics: Algorithmic class and Implementations bargains a special standpoint on desktop studying elements of microarray gene expression dependent melanoma category. This multidisciplinary textual content is on the intersection of laptop technology and biology and, for this reason, can be utilized as a reference e-book by means of researchers and scholars from either fields. every one bankruptcy describes the method of set of rules layout from commencing to finish and goals to notify readers of top practices to be used of their personal learn.

Show description

Read or Download Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations PDF

Best bioinformatics books

Bioinformatics Sequence and Genome Analysis by David Mount PDF

As extra species' genomes are sequenced, computational research of those facts has turn into more and more very important. the second one, fullyyt up to date version of this greatly praised textbook presents a entire and demanding exam of the computational tools wanted for interpreting DNA, RNA, and protein facts, in addition to genomes.

Cancer Proteomics: From Bench to Bedside (Cancer Drug - download pdf or read online

This e-book covers present issues on the topic of using proteomic techniques in melanoma remedy in addition to expected demanding situations that can come up from its program in day-by-day perform. It information present applied sciences utilized in proteomics, examines the use proteomics in phone signaling, offers scientific purposes of proteomics in melanoma remedy, and appears on the position of the FDA in regulating using proteomics.

Download e-book for iPad: ACRI ’96: Proceedings of the Second Conference on Cellular by S. Bandini, G. Mauri (auth.)

ACRI'96 is the second one convention on mobile Automata for learn and undefined; the 1st one was once held in Rende (Cosenza), on September 29-30, 1994. This moment variation confirms the starting to be curiosity in mobile Automata presently current either within the clinical neighborhood and in the business functions global.

Extra resources for Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations

Sample text

Ford, J. , & Pearlman, J. (2005). HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics (Oxford, England), 21(8), 1530–1537. 1093/bioinformatics/bti192 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Cancer Cell, 1(2), 203–209. -N. (2009). kNN: k-nearest neighbors. In X. Wu, & V. ), The top ten algorithms in data mining (pp. 151-162). Boca Raton, FL: Chapman & Hall/CRC Press. , & Tibshirani, R. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics (Oxford, England), 17(6), 520–525. , & Bengio, Y. (2002). K-local hyperplane and convex distance nearest neighbor algorithms. In Dietterich, T. , & Ghahramani, Z. ), Advances in Neural Information Processing Systems, 14 (pp.

Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In A. Prieditis, & S. J. ), Proceedings of the 12th International Conference on Machine Learning, Tahoe City, CA (pp. 194-202). San Francisco, CA: Morgan Kaufmann. Everitt, B. (2006). ). Cambridge, UK: Cambridge University Press. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. 010 Fayyad, U. , & Irani, K. B. (1993). Multi-interval discretization of continuousvalued attributes for classification learning.

Download PDF sample

Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations by Oleg Okun

by Charles

Rated 5.00 of 5 – based on 49 votes