Titlebook: Biological and Medical Data Analysis; 7th International Sy Nicos Maglaveras,Ioanna Chouvarda,Rüdiger Brause Conference proceedings 2006 Spr

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/187494.jpg

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Die autologe Chondrozytentransplantationthat important property and other physiological roles undertaken by the GPCR family, they have been an important target of therapeutic drugs. The function of many GPCRs is not known and accurate classification of GPCRs can help us to predict their function. In this study we suggest a kernel based me

GROG

Tissue Engineering in Cartilage Repairt according to their similarities, and they do not require any class label information. In recent years, various methods for ensemble selection and clustering result combinations have been designed to optimize clustering results. Moreover, conducting data analysis using multiple sources, given the c

effrontery

cortex

construct

R. P. Jakob,E. Gautier,P. Mainil-Varletfor finding regions with a high enrichment of active molecules compared to non-active ones. In this contribution we demonstrate that a simpler binary version of a descriptor can be used for this task as well with similar classification performance, saving computational and memory resources. To gener

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https://doi.org/10.1007/978-3-642-56361-4namic weights and combiners which dynamically estimate local competence are considered. Few algorithms presented in the literature are shown in accordance with our model. In addition we propose two new methods for combining classifiers. The problem of protein secondary structure prediction was selec

Alcove

J. D. Agneskirchner,A. Burkart,A. B. Imhoffmportant problem. So far it has been tackled by several researchers that apply various statistical and machine learning techniques, achieving high accuracy levels, often over 90%. In this paper we propose a mahine learning approach that can further improve the prediction accuracy. First, we provide