Download Algorithms in Bioinformatics: 10th International Workshop, by Yelena Frid, Dan Gusfield (auth.), Vincent Moulton, Mona PDF

By Yelena Frid, Dan Gusfield (auth.), Vincent Moulton, Mona Singh (eds.)

This e-book constitutes the refereed complaints of the tenth overseas Workshop on Algorithms in Bioinformatics, WABI 2010, held in Liverpool, united kingdom, in September 2010. The 30 revised complete papers awarded have been conscientiously reviewed and chosen from eighty three submissions. The papers are equipped in topical sections on biomolecular constitution: RNA, protein and molecular comparability; comparative genomics; haplotype and genotype research; high-throughput facts research: subsequent new release sequencing and stream cytometry; networks; phylogenetics; and sequences, strings and motifs.

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Extra info for Algorithms in Bioinformatics: 10th International Workshop, WABI 2010, Liverpool, UK, September 6-8, 2010. Proceedings

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Series B 58, 267–288 (1996) 26. : Why proteins r-factors are so large: a self consistent analysis. Proteins 46, 345–354 (2002) 27. : Two state allosteric behaviour in a single domain signalling protein. Science 291, 2429–2433 (2001) 28. : On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Mathematical Programming 106, 25–57 (2006) 29. : Robust regression and lasso. Neural Information Processing Systems, NIPS (2008) Data Structures for Accelerating Tanimoto Queries on Real Valued Vectors Thomas G.

Singh, and B. Berger Num. 613 (A) (B) Fig. 2. Example of ensemble construction and classification. A) PDB structure and the second conformer in the synthetic crystal are in black. The two structures classified by Lasso as variable are shown in light gray and the two as variable due to noise, in dark gray. B) Summary of the algorithm output using synthetic data. RMSD is calculated with respect to the PDB structure. Suitability of the linear model and statistical significance of the regression coefficients were evaluated using standard techniques (R2 and t-test).

Sparse Estimation for Structural Variability 5 25 Conclusions We have introduced a novel technique for analyzing conformational changes that may be present in a real protein crystal. Our method first constructs a highquality, diverse ensemble of structures respresentative of the crystallographic data. We then use a sparse estimation algorithm (Lasso) to distinguish structures that are genuinely variable from those that appear variable due to noise. Unlike previous approaches, our method involves the estimation of variability by operating in the EDM space rather than in the 3-D coordinate space.

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