samuel kaski ghsbipfqzemtkkfi call papers workshop probabilistic modeling machine learning structural systems biology httpwwwcshelsinkifigroupbioinfoeventspmsb06 tuusula finland 1718 june 2006 motivation everongoing growth amount biological data development genomewide measurement technologies shift study individual genes systems view contribute need develop computational techniques learning models data time increase available computational resources enabled new realistic modeling methods adopted bioinformatics targets interest deal complex structured objects sequences 2d 3d structures interaction networks many cases structures naturally described probabilistic graphical models hidden markov models conditional random fields bayesian networks recently approaches combine support vector machines probabilistics models introduced fisher kernels maxmargin markov networks structured svm techniques benefit efficient convex optimization approaches thus potentially wellscaleable large problems bioinformatics increasing amount highthroughput experimental data begins enable use advanced modelling methods bioinformatics systems biology time new computational challenges emerge statistical methods required process data underlying potentially complex statistical patterns discerned spurious patterns created random effects simplest problem calls data normalization statistical hypothesis testing general case one required select model eg gene network best explains data objective aim workshop provide broad look state art probabilistic modeling machine learning methods involving biological structures systems bring together method developers experimentalists working problems encourage submissions bringing forward methods discovering complex structures eg interaction networks moleculecellular structures methods supporting genomewide data analysis nonexhaustive list topics suitable workshop methods algorithms bayesian methods data integrationfusion featuresubspace selection highthroughput methods kernel methods machine learning probabilistic inference structured output prediction applications sequence annotation gene expression gene networks gene prediction metabolic profiling metabolic reconstruction protein structure prediction protein function prediction proteinprotein interaction networks workshop organized european network excellence pascal pattern analysis statistical modelling computational learning belongs thematic programme learning complex structured outputs workshop immediately followed international specialised symposium yeasts issy25 june 1821 2006 theme systems biology yeast models applications submissions invite submit extended abstract maximum four pages formatted according springer lecture notes computer science style email address juhorousu cshelsinkifi special journal issue selected papers currently negotiated important dates april 23 2006 abstract submission deadline may 7 2006 notification acceptance may 31 2006 final version due june 1718 2006 workshop invited speakers confirmed tommi jaakkola mit koji tsuda cbrc aist japan organizing committee florence dalchébuc université devryval dessonne jaakko astola tampere university technology nello cristianini uc davis university bristol liisa holm university helsinki mark girolami university glasgow samuel kaski helsinki university technology matej oresic technical research centre finland juho rousu university helsinki esko ukkonen helsinki institute information technology jeanphilippe vert ecole des mines de paris local organization juho rousu university helsinki samuel kaski helsinki university technology esko ukkonen helsinki institute information technology esa pitkänen university helsinki workshop secretary location workshop held conference hotel gustavelund 15 minute trip helsinkivantaa airport thu 07 aug 2008 015024 0200 uai cfp probabilistic modeling machine learning structural systems biology