The 27 full papers presented together with 18 short papers and 24 invited abstracts were carefully reviewed and selected from 131 submissions. They cover topics such as: biomarker discovery; biomedical databases and data integration; biomedical text mining and ortologies; biomolecular imaging; comparative genomics; computational genetic epidemiology; computational proteomics; data mining and visualization; gene expression analysis; genome analysis; high-performance bio-computing; metagenomics; molecular evolution; molecular modelling and simulation; next-generation sequencing data analysis; pattern discovery and classification; population genetics; software tools and applications; structural biology; and systems biology.
Elements of Stochastic Dynamics aims to meet the growing need to understand and master the subject by introducing fundamentals to researchers who want to explore stochastic dynamics in their fields and serving as a textbook for graduate students in various areas involving stochastic uncertainties. All topics within are presented from an application approach, and may thus be more appealing to users without a background in pure Mathematics. The book describes the basic concepts and theories of random variables and stochastic processes in detail; provides various solution procedures for systems subjected to stochastic excitations; introduces stochastic stability and bifurcation; and explores failures of stochastic systems. The book also incorporates some latest research results in modeling stochastic processes; in reducing the system degrees of freedom; and in solving nonlinear problems. The book also provides numerical simulation procedures of widely-used random variables and stochastic processes.
A large number of exercise problems are included in the book to aid the understanding of the concepts and theories, and may be used for as course homework.