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020 _a9781627031073
024 7 _a10.1007/978-1-62703-107-3
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQH324.2
_b2013 EB
245 0 0 _aData Mining for Systems Biology :
_bMethods and Protocols
_cedited by Hiroshi Mamitsuka, Charles DeLisi, Minoru Kanehisa
250 _a1st edition 2013
264 1 _aTotowa, NJ
_bHumana Press
_c2013
300 _a1 recurso en línea (XII, 279 páginas)
_b77 ilustraciones, 18 ilustraciones a color
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
490 0 _aMethods in Molecular Biology
_x1940-6029
_v939
520 _aThe post-genomic revolution is witnessing the generation of petabytes of data annually, with deep implications ranging across evolutionary theory, developmental biology, agriculture, and disease processes. Data Mining for Systems Biology: Methods and Protocols, surveys and demonstrates the science and technology of converting an unprecedented data deluge to new knowledge and biological insight. The volume is organized around two overlapping themes, network inference and functional inference. Written in the highly successful Methods in Molecular Biology™ series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and key tips on troubleshooting and avoiding known pitfalls.   Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols also seeks to aid researchers in the further development of databases, mining and visualization systems that are central to the paradigm altering discoveries being made with increasing frequency.
988 _aSpringer_Protocols_2013
650 7 _2embne
_9160489
_aBioinformática
_vManuales de laboratorio
776 0 8 _iPrinted edition:
_z9781627031066
776 0 8 _iPrinted edition:
_z9781627031080
776 0 8 _iPrinted edition:
_z9781493959129
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-62703-107-3
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b01/2024
_dz
_eb
_zSI