Timo Erkkilä

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MOTIVATION Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have(More)
Endocrine therapy by castration or anti-androgens is the gold standard treatment for advanced prostate cancer. Although it has been used for decades, the molecular consequences of androgen deprivation are incompletely known and biomarkers of its resistance are lacking. In this study, we studied the molecular mechanisms of hormonal therapy by comparing the(More)
We determined longitudinal serum proteomics profiles from children with HLA-conferred diabetes susceptibility to identify changes that could be detected before seroconversion and positivity for disease-associated autoantibodies. Comparisons were made between children who seroconverted and progressed to type 1 diabetes (progressors) and those who remained(More)
BACKGROUND Although endocrine therapy has been used for decades, its influence on the expression of microRNAs (miRNAs) in clinical tissue specimens has not been analyzed. Moreover, the effects of the TMPRSS2:ERG fusion on the expression of miRNAs in hormone naïve and endocrine-treated prostate cancers are poorly understood. METHODS We used clinical(More)
Tampere Verification Tool (TVT) is a collection of programs for automated verification of concurrent and reactive systems. TVT has its roots in process algebras and explicit state space exploration, but in addition to actions, our formalism allows use of state-based information in the form of truth-valued state propositions. Furthermore, it contains three(More)
Monitoring of bacterial populations requires automated analysis tools that provide accurate cell type quantification results. Here, methods for automated image analysis and bacteria type classification are presented. The classification method employs several discriminative features, calculated from automatically segmented images, for class determination.(More)
Cluster analysis has become a standard computational method for gene function discovery as well as for more general explanatory data analysis. A number of different approaches have been proposed for that purpose, out of which different mixture models provide a principled probabilistic framework. Cluster analysis is increasingly often supplemented with(More)
This paper describes our submission to the eighth annual MLSP competition organized by Amazon during the 2012 IEEE MLSP workshop. Our approach is based on a nearest-neighbor-like classifier with a distance metric learned from samples. The method was second in the final standings with prediction accuracy of 81 %, while the winning submission was 87 %(More)
As many real-world applications, microarray measurements are inapplicable for large-scale teaching purposes due to their laborious preparation process and expense. Fortunately, many phases of the array preparation process can be efficiently demonstrated by using a software simulator tool. Here we propose the use of microarray simulator as an aiding tool in(More)
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