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Computational Life Sciences

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Inhalt Systems Biology.- Structural Protein Interactions Predict Kinase-Inhibitor Interactions in Upregulated Pancreas Tumour Gene... Weiterlesen
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Beschreibung

Inhalt

Systems Biology.- Structural Protein Interactions Predict Kinase-Inhibitor Interactions in Upregulated Pancreas Tumour Genes Expression Data.- Biochemical Pathway Analysis via Signature Mining.- Recurrent Neuro-fuzzy Network Models for Reverse Engineering Gene Regulatory Interactions.- Data Analysis and Integration.- Some Applications of Dummy Point Scatterers for Phasing in Macromolecular X-Ray Crystallography.- BioRegistry: A Structured Metadata Repository for Bioinformatic Databases.- Robust Perron Cluster Analysis for Various Applications in Computational Life Science.- Structural Biology.- Multiple Alignment of Protein Structures in Three Dimensions.- Protein Annotation by Secondary Structure Based Alignments (PASSTA).- MAPPIS: Multiple 3D Alignment of Protein-Protein Interfaces.- Genomics.- Frequent Itemsets for Genomic Profiling.- Gene Selection Through Sensitivity Analysis of Support Vector Machines.- The Breakpoint Graph in Ciliates.- Computational Proteomics.- ProSpect: An R Package for Analyzing SELDI Measurements Identifying Protein Biomarkers.- Algorithms for the Automated Absolute Quantification of Diagnostic Markers in Complex Proteomics Samples.- Detection of Protein Assemblies in Crystals.- Molecular Informatics.- Molecular Similarity Searching Using COSMO Screening Charges (COSMO/3PP).- Increasing Diversity in In-silico Screening with Target Flexibility.- Multiple Semi-flexible 3D Superposition of Drug-Sized Molecules.- Molecular Structure Determination and Simulation.- Efficiency Considerations in Solving Smoluchowski Equations for Rough Potentials.- Fast and Accurate Structural RNA Alignment by Progressive Lagrangian Optimization.- Visual Analysis of Molecular Conformations by Means of a Dynamic Density Mixture Model.- Distributed Data Mining.- Distributed BLAST in a Grid Computing Context.- Parallel Tuning of Support Vector Machine Learning Parameters for Large and Unbalanced Data Sets.- The Architecture of a Proteomic Network in the Yeast.

Produktinformationen

Titel: Computational Life Sciences
Untertitel: First International Symposium, CompLife 2005, Konstanz, Germany, September 25-27, 2005, Proceedings
Editor:
EAN: 9783540317265
Digitaler Kopierschutz: Wasserzeichen
Format: E-Book (pdf)
Hersteller: Springer Berlin Heidelberg
Genre: Biologie
Anzahl Seiten: 280
Veröffentlichung: 28.09.2005
Dateigrösse: 12.9 MB

Weitere Bände aus der Buchreihe "Lecture Notes in Bioinformatics"