By Mario Cannataro, Pietro Hiram Guzzi (auth.), Riccardo Rizzo, Paulo J. G. Lisboa (eds.)
This publication 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, awarded including 2 keynote speeches and 1 instructional, have been rigorously reviewed and chosen from 24 submissions. The papers are geared up in topical sections on series research, promoter research and id of transcription issue binding websites; equipment for the unsupervised research, validation and visualization of constructions found in bio-molecular information -- prediction of secondary and tertiary protein constructions; gene expression facts research; bio-medical textual content mining and imaging -- equipment for analysis and diagnosis; mathematical modelling and simulation of organic structures; and clever medical selection aid platforms (i-CDSS).
Read Online 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 info for Computational Intelligence Methods for Bioinformatics and Biostatistics: 7th International Meeting, CIBB 2010, Palermo, Italy, September 16-18, 2010, Revised Selected Papers
Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology. PLoS Comput. Biol. 4, e1000029 (2008) 9. : Dictionary of distances. Elsevier, Amsterdam (2006) 10. : How does gene expression cluster work? Nature Biotechnology 23, 1499–1501 (2006) 11. : Genclust: A genetic algorithm for clustering gene expression data. BMC Bioinformatics 6, 289 (2005) 12. : A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology 3 (2002) 13.
Training is performed by gradient descent on the error, which we model as the relative entropy between the target class and the output of the network. The overall output of the network (output layer of N (o) ()) is implemented as a softmax function, while all internal squashing functions are implemented as hyperbolic tangents. Training terminates when either the walltime on the server is reached (6 days for fungi and plants, 10 days for animals) or the epoch limit is De Novo Protein Subcellular Localization Prediction 37 reached (40k for fungi and plants and 20k for animals).
The accuracy of this new version of SCL pred is shown in Table 6. We then retest this version of SCL pred on the SP 57 (a subset of Swiss-Prot 57, described in the dataset section) and again compare its accuracy with BaCelLo, LOCtree, WoLF PSORT, Protein Prowler and TARGETp (Table 7). We obtained results for WoLF PSORT and Protein Prowler through their respective web servers, and results for TARGETp were obtained by downloading the stand alone version of TARGETp available from the TARGETp website, which we then ran locally, hence we have no control on the sequence identity cutoﬀs between the training sets of these predictors and SP 57.
Computational Intelligence Methods for Bioinformatics and Biostatistics: 7th International Meeting, CIBB 2010, Palermo, Italy, September 16-18, 2010, Revised Selected Papers by Mario Cannataro, Pietro Hiram Guzzi (auth.), Riccardo Rizzo, Paulo J. G. Lisboa (eds.)