Blar i forfatter "Troyanskaya, Olga"

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    • Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields 

      Barutcuoglu, Z; Airoldi, EM; Dumeaux, vanessa; Schapire, RE; Troyanskaya, Olga (Journal article; Tidsskriftartikkel; Peer reviewed, 2009)
      The heterogeneity of cancer cannot always be recognized by tumor morphology, but may be reflected by the underlying genetic aberrations. Array comparative genome hybridization (array-CGH) methods provide high-throughput data on genetic copy numbers, but determining the clinically relevant copy number changes remains a challenge. Conventional classification methods for linking recurrent alterations ...

    • Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes 

      Park, Christopher Y.; Wong, Aaron K.; Greene, Casey S.; Rowland, Jessica; Guan, Yuanfang; Bongo, Lars Ailo; Burdine, Rebecca D.; Troyanskaya, Olga (Journal article; Tidsskriftartikkel; Peer reviewed, 2013)
      A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not ...

    • IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks 

      Wong, Aaron K.; Park, Christopher Y.; Greene, Casey S.; Bongo, Lars Ailo; Guan, Yuanfang; Troyanskaya, Olga (Journal article; Tidsskriftartikkel; Peer reviewed, 2012)
      Integrative multi-species prediction (IMP) is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides a framework for biologists to analyze their candidate gene sets in the context of functional networks, as they expand or ...