LEADER 00000cam a2200673Ka 4500
001 ocn670430124
003 OCoLC
005 20160527040639.7
006 m o d
007 cr cnu---unuuu
008 101018s2010 si a ob 001 0 eng d
010 2010281919
019 729020618|a764546263|a816583175|a872127911
020 9789814287319|q(electronic book)
020 9814287318|q(electronic book)
020 |z9789814287302
020 |z981428730X
035 (OCoLC)670430124|z(OCoLC)729020618|z(OCoLC)764546263
|z(OCoLC)816583175|z(OCoLC)872127911
040 N$T|beng|epn|cN$T|dYDXCP|dE7B|dOCLCQ|dEBLCP|dI9W|dOCLCQ
|dDEBSZ|dOCLCQ|dDKDLA|dOCLCO|dOCLCQ|dOCLCF|dOCLCQ|dIDEBK
|dOCLCQ
049 RIDW
050 4 QH324.25|b.Y26 2010eb
072 7 COM|x082000|2bisacsh
082 04 572.80285518|222
090 QH324.25|b.Y26 2010eb
100 1 Yang, Zheng Rong.|0https://id.loc.gov/authorities/names/
nb2004305210
245 10 Machine learning approaches to bioinformatics /|cZheng
Rong Yang.
264 1 Singapore ;|aHackensack, NJ :|bWorld Scientific,|c[2010]
264 4 |c©2010
300 1 online resource (xiv, 322 pages) :|billustrations.
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
340 |gpolychrome|2rdacc
347 text file|2rdaft
490 1 Science, engineering, and biology informatics ;|vv. 4
504 Includes bibliographical references and index.
505 0 1. Introduction. 1.1. Brief history of bioinformatics.
1.2. Database application in bioinformatics. 1.3. Web
tools and services for sequence homology alignment. 1.4.
Pattern analysis. 1.5. The contribution of information
technology. 1.6. Chapters -- 2. Introduction to
unsupervised learning -- 3. Probability density estimation
approaches. 3.1. Histogram approach. 3.2. Parametric
approach. 3.3. Non-parametric approach -- 4. Dimension
reduction. 4.1. General. 4.2. Principal component
analysis. 4.3. An application of PCA. 4.4. Multi-
dimensional scaling. 4.5. Application of the Sammon
algorithm to gene data -- 5. Cluster analysis. 5.1.
Hierarchical clustering. 5.2. K-means. 5.3. Fuzzy C-means.
5.4. Gaussian mixture models. 5.5. Application of
clustering algorithms to the Burkholderia pseudomallei
gene expression data -- 6. Self-organising map. 6.1.
Vector quantization. 6.2. SOM structure. 6.3. SOM learning
algorithm. 6.4. Using SOM for classification. 6.5.
Bioinformatics applications of VQ and SOM. 6.6. A case
study of gene expression data analysis. 6.7. A case study
of sequence data analysis -- 7. Introduction to supervised
learning. 7.1. General concepts. 7.2. General definition.
7.3. Model evaluation. 7.4. Data organisation. 7.5. Bayes
rule for classification -- 8. Linear/quadratic
discriminant analysis and K-nearest neighbour. 8.1. Linear
discriminant analysis. 8.2. Generalised discriminant
analysis. 8.3. K-nearest neighbour. 8.4. KNN for gene data
analysis -- 9. Classification and regression trees, random
forest algorithm. 9.1. Introduction. 9.2. Basic principle
for constructing a classification tree. 9.3.
Classification and regression tree. 9.4. CART for compound
pathway involvement prediction. 9.5. The random forest
algorithm. 9.6. RF for analyzing Burkholderia pseudomallei
gene expression profiles -- 10. Multi-layer perceptron.
10.1. Introduction. 10.2. Learning theory. 10.3. Learning
algorithms. 10.4. Applications to bioinformatics. 10.5. A
case study on Burkholderia pseudomallei gene expression
data -- 11. Basis function approach and vector machines.
11.1. Introduction. 11.2. Radial-basis function neural
network (RBFNN). 11.3. Bio-basis function neural network.
11.4. Support vector machine. 11.5. Relevance vector
machine -- 12. Hidden Markov model. 12.1. Markov model.
12.2. Hidden Markov model. 12.3. HMM for sequence
classification -- 13. Feature selection. 13.1. Built-in
strategy. 13.2. Exhaustive strategy. 13.3. Heuristic
strategy -- orthogonal least square approach. 13.4.
Criteria for feature selection -- 14. Feature extraction
(biological data coding). 14.1. Molecular sequences. 14.2.
Chemical compounds. 14.3. General definition. 14.4.
Sequence analysis -- 15. Sequence/structural
bioinformatics foundation -- peptide classification. 15.1.
Nitration site prediction. 15.2. Plant promoter region
prediction -- 16. Gene network -- causal network and
Bayesian networks. 16.1. Gene regulatory network. 16.2.
Causal networks, networks, graphs. 16.3. A brief review of
the probability. 16.4. Discrete Bayesian network. 16.5.
Inference with discrete Bayesian network. 16.6. Learning
discrete Bayesian network. 16.7. Bayesian networks for
gene regulartory networks. 16.8. Bayesian networks for
discovering peptide patterns. 16.9. Bayesian networks for
analysing Burkholderia pseudomallei gene data -- 17. S-
systems. 17.1. Michealis-Menten change law. 17.2. S-
system. 17.3. Simplification of an S-system. 17.4.
Approaches for structure identification and parameter
estimation. 17.5. Steady-state analysis of an S-system.
17.6. Sensitivity of an S-system -- 18. Future directions.
18.1. Multi-source data. 18.2. Gene regulatory network
construction. 18.3. Building models using incomplete data.
18.4. Biomarker detection from gene expression data.
520 This book covers a wide range of subjects in applying
machine learning approaches for bioinformatics projects.
The book succeeds on two key unique features. First, it
introduces the most widely used machine learning
approaches in bioinformatics and discusses, with
evaluations from real case studies, how they are used in
individual bioinformatics projects. Second, it introduces
state-of-the-art bioinformatics research methods. The
theoretical parts and the practical parts are well
integrated for readers to follow the existing procedures
in individual research. Unlike most of the bioinformatics
books on the market, the content coverage is not limited
to just one subject. A broad spectrum of relevant topics
in bioinformatics including systematic data mining and
computational systems biology researches are brought
together in this book, thereby offering an efficient and
convenient platform for teaching purposes. An essential
reference for both final year undergraduates and graduate
students in universities, as well as a comprehensive
handbook for new researchers, this book will also serve as
a practical guide for software development in relevant
bioinformatics projects.
588 0 Print version record.
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
650 0 Bioinformatics.|0https://id.loc.gov/authorities/subjects/
sh00003585
650 0 Machine learning.|0https://id.loc.gov/authorities/subjects
/sh85079324
650 0 Bioinformatics|0https://id.loc.gov/authorities/subjects/
sh00003585|vCase studies.|0https://id.loc.gov/authorities/
subjects/sh99001484
650 0 Machine learning|0https://id.loc.gov/authorities/subjects/
sh85079324|vCase studies.|0https://id.loc.gov/authorities/
subjects/sh99001484
650 7 Bioinformatics.|2fast|0https://id.worldcat.org/fast/832181
650 7 Machine learning.|2fast|0https://id.worldcat.org/fast/
1004795
655 0 Electronic books.
655 4 Electronic books.
655 7 Case studies.|2fast|0https://id.worldcat.org/fast/1423765
655 7 Case studies.|2lcgft|0https://id.loc.gov/authorities/
genreForms/gf2017026140
776 08 |iPrint version:|aYang, Zheng Rong.|tMachine learning
approaches to bioinformatics.|dSingapore ; Hackensack, NJ
: World Scientific, ©2010|z9789814287302|w(OCoLC)619946410
830 0 Science, engineering, and biology informatics ;|0https://
id.loc.gov/authorities/names/no2007052160|vv. 4.
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=340803|zOnline eBook. Access restricted to
current Rider University students, faculty, and staff.
856 42 |3Instructions for reading/downloading this eBook|uhttp://
guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d20160616|cEBSCO|tebscoebooksacademic|lridw
994 92|bRID
