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This work covers a range of advanced topics in genomics, data mining, molecular simulation, molecular informatics, systems biology, and computational neuroscience. It discusses improved robustness in time series analysis of gene expression data through polynomial model-based clustering and explores hybrid grid applications for orthologous groups clustering. Techniques for promoter prediction using DNA's physico-chemical properties and parametric spectral analysis of malaria gene expression data are examined. The text also presents algorithms for identifying long conserved regions between genes and addresses the reversal median problem in mitochondrial gene orders. In data mining, it highlights the construction of predictive models using data assimilation and introduces set-oriented dimension reduction via hidden Markov models. The applicability of loop recombination in ciliates is evaluated, alongside statistical methods for detecting signals in MALDI-TOF spectra. Molecular simulation topics include chaotic transitions in high-dimensional dynamics and the effects of solvents on tripeptide stability. The analysis of single molecule force spectroscopy data and a point-matching algorithm for 3D surface alignment of drug-sized molecules are discussed in molecular informatics. In systems biology, the text presents adaptive modeling approaches for pharmacokinetics and new methods for analyzing metabolic networks. It also covers s
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Computational life sciences II, Michael Berthold
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- Année de publication
- 2006
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