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  • Discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on global approaches such as clustering. An alternative way is to utilize local pattern mining techniques for global modelling and knowledge discovery. Nevertheless, moving from local patterns to models and knowledge is still a challenge due to the overwhelming number of local patterns and their summarization remains an open issue. This paper is an attempt to fulfill this need: thanks to recent progress in constraint-based paradigm, it proposes three data mining methods to deal with the use of local patterns by highlighting the most promising ones or summarizing them. Ideas at the core of these processes are removing redundancy, integrating background knowledge and recursive mining.
  • Discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on global approaches such as clustering. An alternative way is to utilize local pattern mining techniques for global modelling and knowledge discovery. Nevertheless, moving from local patterns to models and knowledge is still a challenge due to the overwhelming number of local patterns and their summarization remains an open issue. This paper is an attempt to fulfill this need: thanks to recent progress in constraint-based paradigm, it proposes three data mining methods to deal with the use of local patterns by highlighting the most promising ones or summarizing them. Ideas at the core of these processes are removing redundancy, integrating background knowledge and recursive mining. (en)
  • Získávání srozumitelné znalosti z dat genové exprese se tradičně provádí globálními metodami jako jsou shlukování nebo statistická analýza. Alternativním postupem je dolování lokálních vzorů. Kandidátských lokálních vzorů je ale pro manuální analýzu příliš a je nutné provést jejich automatický předvýběr. Tato kapitola k tomu využívá omezujících podmínek definovaných na základě genomické apriorní znalosti. Kromě teoretického rámce je součástí kapitoly i případová studie provedená na SAGE datech. (cs)
Title
  • Discovering Knowledge from Local Patterns in SAGE Data
  • Získávání znalostí z lokálních vzorů v SAGE datech (cs)
  • Discovering Knowledge from Local Patterns in SAGE Data (en)
skos:prefLabel
  • Discovering Knowledge from Local Patterns in SAGE Data
  • Získávání znalostí z lokálních vzorů v SAGE datech (cs)
  • Discovering Knowledge from Local Patterns in SAGE Data (en)
skos:notation
  • RIV/68407700:21230/09:03151580!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 310694
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/09:03151580
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • constraints; data mining; genomics; knowledge; pattern (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [12D74EE1C299]
http://linked.open...i/riv/mistoVydani
  • Hershey
http://linked.open...i/riv/nazevZdroje
  • Data Mining and Medical Knowledge Management: Cases and Applications
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...v/pocetStranKnihy
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kléma, Jiří
  • Cremilleux, B.
  • Soulet, A.
  • Celine, H.
  • Gandrillion, O.
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • IGI Publishing
https://schema.org/isbn
  • 978-1-60566-218-3
http://localhost/t...ganizacniJednotka
  • 21230
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