. "http://www.isvav.cz/projectDetail.do?rowId=KJB201210501"^^ . . "0"^^ . " knowledge discovery in databases" . "In the current raise of interest in the research on gene relationship discovery from gene expression data by means of machine learning and data mining, logic-based relational machine learning (LBRML) algorithms receive little or no attention, which contrasts with their successes in related biological applications, their strong theoretical foundations, the availability of a plethora of implementations, and mainly the understandability and direct biological interpretability of their outputs. Their little penetration is due to the fact that in comparison to statistical approaches currently favored in this application field, LBRML exhibits insufficient robustness agains data imperfection, inefficiency in the attribute-rich genetic domains and insufficient uncertainty modeling features. We will eliminate these algorithmic defficiencies by incorporating probabilistic inference/representation techniques into LBRML and demonstrate experimentally its power in the gene relationship discovery."@en . "KJB201210501" . . . . "V sou\u010Dasn\u00E9 vln\u011B z\u00E1jmu o objevov\u00E1n\u00ED souvislost\u00ED z dat genov\u00E9 exprese prost\u0159edky strojov\u00E9ho u\u010Den\u00ED (SU) a data miningu nemaj\u00ED algoritmy rela\u010Dn\u00EDho strojov\u00E9ho u\u010Den\u00ED zalo\u017Een\u00E9ho na logice (RSUZL) t\u00E9m\u011B\u0159 \u017E\u00E1dnou pozornost, co\u017E kontrastuje s jejich dobr\u00FDmi v\u00FDsledky v jin\u00FDch biologick\u00FDch aplikac\u00EDch, jejich siln\u00FDm teoretick\u00FDm z\u00E1klad\u016Fm, dostupnost\u00ED implementac\u00ED jejich rozmanit\u00FDch algoritm\u016F a zejm. srozumitelnost\u00ED jejich v\u00FDstup\u016F a mo\u017Enost\u00ED je p\u0159\u00EDmo biologicky interpretovat. D\u016Fvodem jejich nevyu\u017Eit\u00ED je, \u017Ee oproti statistick\u00FDm p\u0159\u00EDstup\u016Fm v t\u00E9to aplika\u010Dn\u00ED oblasti zat\u00EDm preferovan\u00FDm vykazuj\u00ED v\u00FD\u0161e zm\u00EDn\u011Bn\u00E9 algoritmy malou robustnost v\u016F\u010Di chyb\u00E1m v datech, n\u00EDzkou efektivitu v mnohaatributov\u00FDch genetick\u00FDch dom\u00E9n\u00E1ch a disponuj\u00ED nedostate\u010Dn\u00FDmi prost\u0159edky pro modelov\u00E1n\u00ED neur\u010Ditosti. Tyto algoritmick\u00E9 nedostatky odstran\u00EDme implementac\u00ED pravd\u011Bpodobnostn\u00ED inference a reprezentace do algoritm\u016F RSUZL a experiment\u00E1ln\u011B p\u0159edvedeme jeho s\u00EDlu v oblasti objevov\u00E1n\u00ED souvislost\u00ED mezi geny." . . "Logic-based machine learning for genomic data analysis"@en . " inductive logic programming" . . . "Logick\u00E9 strojov\u00E9 u\u010Den\u00ED pro anal\u00FDzu genomick\u00FDch dat" . . . "Existing algorithms of logic-based relational machine learning were enhanced and new algorithms were developed, both for the sake of discovering unknown biological principles, primarily from gene expression data measured by DNA chips."@en . "Byly zdokonaleny existuj\u00EDc\u00ED algoritmy rela\u010Dn\u00EDho strojov\u00E9ho u\u010Den\u00ED zalo\u017Een\u00E9 na logice a vyvinuty nov\u00E9 algoritmy pou\u017Eiteln\u00E9 pro \u00FA\u010Del nal\u00E9z\u00E1n\u00ED nezn\u00E1m\u00FDch biologick\u00FDch z\u00E1konitost\u00ED z genomick\u00FDch dat, zejm\u00E9na z dat genov\u00E9 exprese m\u011B\u0159en\u00FDch DNA \u010Dipy."@cs . . "machine learning" . "2009-04-03+02:00"^^ . "8"^^ . . . "0"^^ . "8"^^ . . . "1"^^ . "machine learning; knowledge discovery in databases; inductive logic programming; gene expression data"@en . . . . .