By Mario Cannataro, Pietro Hiram Guzzi (auth.), Riccardo Rizzo, Paulo J. G. Lisboa (eds.)
This booklet constitutes the completely refereed post-proceedings of the seventh foreign assembly on Computational Intelligence equipment for Bioinformatics and Biostatistics, CIBB 2010, held in Palermo, Italy, in September 2010.
The 19 papers, provided including 2 keynote speeches and 1 educational, have been rigorously reviewed and chosen from 24 submissions. The papers are equipped in topical sections on series research, promoter research and identity of transcription issue binding websites; tools for the unsupervised research, validation and visualization of buildings found in bio-molecular information -- prediction of secondary and tertiary protein constructions; gene expression info research; bio-medical textual content mining and imaging -- equipment for prognosis and analysis; mathematical modelling and simulation of organic structures; and clever medical choice help structures (i-CDSS).
Read or Download Computational Intelligence Methods for Bioinformatics and Biostatistics: 7th International Meeting, CIBB 2010, Palermo, Italy, September 16-18, 2010, Revised Selected Papers PDF
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Additional resources for Computational Intelligence Methods for Bioinformatics and Biostatistics: 7th International Meeting, CIBB 2010, Palermo, Italy, September 16-18, 2010, Revised Selected Papers
6073, pp. 125–138. Springer, Heidelberg (2010) 17. : Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer. BMC Bioinformatics 9, 462 (2008) 18. : Statistical Indices for Computational and Data Driven Class Discovery in Microarray Data. In: Biological Data Mining, pp. 295–335. CRC Press, Boca Raton (2009) 19. : Speeding up the Consensus Clustering methodology for microarray data analysis.
Estimating the number of clusters in a dataset via the gap statistics. Journal Royal Statistical Society B 2, 411–423 (2001) 35. 2915v1 36. : Clustering gene expression data using a graph-theoretic approach: An application of minimum spanning tree. Bioinformatics 18, 526–535 (2002) 37. : Cluster Analysis of Gene Expression Data. D. thesis, University of Washington (2001) 38. : Validating clustering for gene expression data. Bioinformatics 17, 309–318 (2001) De Novo Protein Subcellular Localization Prediction by N-to-1 Neural Networks Catherine Mooney1 , Yong-Hong Wang2 , and Gianluca Pollastri1, 1 Complex and Adaptive Systems Laboratory and School of Computer Science and Informatics, University College Dublin, Belﬁeld, Dublin 4 2 Biophysics Institute, Hebei University of Technology, Tianjin, China Abstract.
22. : The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982) 23. : Data Clustering: a Review. ACM Computing Surveys 31, 264–323 (1999) 24. : Algorithms for Clustering Data. Prentice-Hall, Englewood Cliﬀs (1988) 25. : Biological cluster evaluation for gene function prediction. Journal of Computational Biology 17, 1–18 (2010) 26. : A highly eﬃcient multi-core algorithm for clustering extremely large datasets. BMC Bioinformatics 11, 169 (2010) 27.