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LEADER 00000cam  2200757Ii 4500 
001    869218626 
003    OCoLC 
005    20181130031233.4 
006    m     o  d         
007    cr cnu---unuuu 
008    140127s2013    gw a    ob    000 0 eng d 
019    871224049|a874156190|a875633048|a1005788322|a1058621085
       |a1066476318 
020    9783642451614|q(electronic bk.) 
020    3642451616|q(electronic bk.) 
020    3642451608 
020    9783642451607 
020    |z9783642451607 
024 7  10.1007/978-3-642-45161-4|2doi 
035    (OCoLC)869218626|z(OCoLC)871224049|z(OCoLC)874156190
       |z(OCoLC)875633048|z(OCoLC)1005788322|z(OCoLC)1058621085
       |z(OCoLC)1066476318 
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049    MAIN 
050  4 QH441.2 
060  4 QU 470 
060  4 2014 D-555 
072  7 PSA|2bicssc 
072  7 SCI086000|2bisacsh 
072  7 SCI064000|2bisacsh 
072  7 SCI|x007000|2bisacsh 
072  7 QH|2lcco 
082 04 572.80285|223 
245 00 Gene network inference :|bverification of methods for 
       systems genetics data /|cAlberto de la Fuente, editor. 
264  1 Heidelberg :|bSpringer,|c2013. 
300    1 online resource (xi, 130 pages) :|billustrations (some 
       color) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
340    |gpolychrome|2rdacc|0http://rdaregistry.info/termList/
       RDAColourContent/1003 
347    text file|2rdaft|0http://rdaregistry.info/termList/
       fileType/1002 
504    Includes bibliographical references at the end of each 
       chapters. 
505 0  Simulation of the Benchmark Datasets -- A Panel of 
       Learning Methods for the Reconstruction of Gene Regulatory
       Networks in a Systems Genetics Context -- Benchmarking a 
       simple yet effective approach for inferring gene 
       regulatory networks from systems genetics data -- 
       Differential Equation based reverse-engineering algorithms
       : pros and cons -- Gene regulatory network inference from 
       systems genetics data using tree-based methods -- 
       Extending partially known networks -- Integration of 
       genetic variation as external perturbation to reverse 
       engineer regulatory networks from gene expression data -- 
       Using Simulated Data to Evaluate Bayesian Network Approach
       for Integrating Diverse Data. 
520    This book presents recent methods for Systems Genetics 
       (SG) data analysis, applying them to a suite of simulated 
       SG benchmark datasets. Each of the chapter authors 
       received the same datasets to evaluate the performance of 
       their method to better understand which algorithms are 
       most useful for obtaining reliable models from SG 
       datasets. The knowledge gained from this benchmarking 
       study will ultimately allow these algorithms to be used 
       with confidence for SG studies e.g. of complex human 
       diseases or food crop improvement. The book is primarily 
       intended for researchers with a background in the life 
       sciences, not for computer scientists or statisticians. 
588 0  Online resource; title from PDF title page (SpringerLink, 
       viewed January 6, 2014). 
588 0  Print version record. 
650  0 Genetics|0http://id.loc.gov/authorities/subjects/
       sh85053877|xData processing.|0http://id.loc.gov/
       authorities/subjects/sh99005487 
650  0 Systems biology.|0http://id.loc.gov/authorities/subjects/
       sh2008002926 
650  0 Bioinformatics.|0http://id.loc.gov/authorities/subjects/
       sh00003585 
650 12 Gene Regulatory Networks|xgenetics.|0https://
       id.nlm.nih.gov/mesh/D053263Q000235 
650 14 Life Sciences. 
650 22 Algorithms.|0https://id.nlm.nih.gov/mesh/D000465 
650 22 Gene Expression Profiling.|0https://id.nlm.nih.gov/mesh/
       D020869 
650 22 Oligonucleotide Array Sequence Analysis.|0https://
       id.nlm.nih.gov/mesh/D020411 
650 22 Systems Biology|xmethods.|0https://id.nlm.nih.gov/mesh/
       D049490Q000379 
650 24 Systems Biology. 
650 24 Bioinformatics. 
650 24 Biological Networks, Systems Biology. 
650 24 Computer Appl. in Life Sciences. 
650 24 Gene Expression. 
655  4 Electronic books. 
700 1  Fuente, Alberto de la|c(Geneticist),|0http://id.loc.gov/
       authorities/names/n2014184526|eeditor. 
776 08 |iPrint version:|tGene network inference : verification of
       methods for systems genetics data.|dHeidelberg, Germany : 
       Springer, 2013|hxi, 130 pages|z9783642451607 
990    SpringerLink|bSpringer English/International eBooks 2013 -
       Full Set|c2018-10-31|yNew collection 
       springerlink.ebooks2013|5OH1 
990    SpringerLink|bSpringer English/International eBooks 2013 -
       Full Set|c2018-11-30|yMaster record variable field(s) 
       change: 650|5OH1 
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