Kooperativer Bibliotheksverbund

Berlin Brandenburg

and
and

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    In: Chemoinformatics and Advanced Machine Learning Perspectives, Chapter 2, pp.16-34
    Description: Prediction models for absorption, distribution, metabolic and excretion properties of chemical compounds play a crucial rule in the drug discovery process. Often such models are derived via machine learning techniques. Kernel based learning algorithms, like the well known support vector machine (SVM) have gained a growing interest during the last years for this purpose. One of the key concepts of SVMs is a kernel function, which can be thought of as a special similarity measure. In this Chapter the author describes optimal assignment kernels for multi-labeled molecular graphs. The optimal assignment kernel is based on the idea of a maximal weighted bipartite matching of the atoms of a pair of molecules. At the same time the physico-chemical properties of each single atom are considered as well as the neighborhood in the molecular graph. Later on our similarity measure is extended to deal with reduced graph representations, in which certain structural elements, like rings, donors or acceptors, are condensed in one single node of the graph. Comparisons of the optimal assignment kernel with other graph kernels as well as with classical descriptor based models show a significant improvement in prediction accuracy.
    ISBN: 9781615209118
    Source: IGI Global
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    Language: English
    In: Proceedings Of The 5th Asia-Pacific Bioinformatics Conference, 2007, pp.267-276
    Description: Abstract Inferring the structure of gene regulatory networks from gene expression data has attracted a growing interest during the last years. Several machine learning related methods, such as Bayesian networks, have been proposed to deal with this challenging problem. However, in many cases, network reconstructions purely based on gene expression data not lead to satisfactory results when comparing the obtained topology against a validation network. Therefore, in this paper we propose an "inverse" approach: Starting from a priori specified network topologies, we identify those parts of the network which are relevant for the gene expression data at hand. For this purpose, we employ linear ridge regression to predict the expression level of a given gene from its relevant regulators with high reliability. Calculated statistical significances of the resulting network topologies reveal that slight modifications of the pruned regulatory network enable an additional substantial improvement.
    Keywords: Contributed Papers
    ISBN: 9781860947995
    Source: World Scientific Books (World Scientific Publishing Co.)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    Language: German
    In: Übernahme börsennotierter Unternehmen : Strategie, Unternehmensbewertung, rechtliche Rahmenbedingungen, Steuern und Finanzkommunikation, pp.117-179
    ISBN: 3-7910-2040-4
    Source: Deutsche Zentralbibliothek für Wirtschaftswissenschaften
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Language: English
    In: Proceedings Of The 5th Asia-Pacific Bioinformatics Conference, 2007, pp.247-256
    Description: Abstract The ability to measure the transcriptional response after a stimulus has drawn much attention to the underlying gene regulatory networks. Several machine learning related methods, such as Bayesian networks and decision trees, have been proposed to deal with this difficult problem, but rarely a systematic comparison between different algorithms has been performed. In this work, we critically evaluate the application of multiple linear regression, SVMs, decision trees and Bayesian networks to reconstruct the budding yeast cell cycle network. The performance of these methods is assessed by comparing the topology of the reconstructed models to a validation network. This validation network is defined a priori and each interaction is specified by at least one publication. We also investigate the quality of the network reconstruction if a varying amount of gene regulatoly dependencies is provided a priori .
    Keywords: Contributed Papers
    ISBN: 9781860947995
    Source: World Scientific Books (World Scientific Publishing Co.)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: German
    In: Marketing im Perspektivenwechsel: Festschrift für Udo Koppelmann, pp.147-162
    Keywords: Economics/Management Science ; Marketing ; Business
    ISBN: 9783540262909
    ISBN: 3540262903
    Source: SpringerLink Books
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages