000 03030nam a22005055i 4500
001 978-3-642-45161-4
003 DE-He213
005 20140220082923.0
007 cr nn 008mamaa
008 140103s2013 gw | s |||| 0|eng d
020 _a9783642451614
_9978-3-642-45161-4
024 7 _a10.1007/978-3-642-45161-4
_2doi
050 4 _aQH301-705
072 7 _aPSA
_2bicssc
072 7 _aSCI086000
_2bisacsh
072 7 _aSCI064000
_2bisacsh
082 0 4 _a570
_223
100 1 _aFuente, Alberto.
_eeditor.
245 1 0 _aGene Network Inference
_h[electronic resource] :
_bVerification of Methods for Systems Genetics Data /
_cedited by Alberto Fuente.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXI, 130 p. 49 illus., 33 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aSimulation 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 _aThis 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.
650 0 _aLife sciences.
650 0 _aGene expression.
650 0 _aBioinformatics.
650 0 _aBiological models.
650 0 _aBiology
_xData processing.
650 1 4 _aLife Sciences.
650 2 4 _aSystems Biology.
650 2 4 _aBioinformatics.
650 2 4 _aBiological Networks, Systems Biology.
650 2 4 _aComputer Appl. in Life Sciences.
650 2 4 _aGene Expression.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642451607
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-45161-4
912 _aZDB-2-SBL
999 _c98848
_d98848