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Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 7th International Me Riccardo Rizzo,Paulo J. G. Lisboa Conference

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發(fā)表于 2025-3-21 16:31:44 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Computational Intelligence Methods for Bioinformatics and Biostatistics
副標(biāo)題7th International Me
編輯Riccardo Rizzo,Paulo J. G. Lisboa
視頻videohttp://file.papertrans.cn/233/232392/232392.mp4
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 7th International Me Riccardo Rizzo,Paulo J. G. Lisboa Conference
描述This book constitutes the thoroughly refereed post-proceedings of the 7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2010, held in Palermo, Italy, in September 2010. The 19 papers, presented together with 2 keynote speeches and 1 tutorial, were carefully reviewed and selected from 24 submissions. The papers are organized in topical sections on sequence analysis, promoter analysis and identification of transcription factor binding sites; methods for the unsupervised analysis, validation and visualization of structures discovered in bio-molecular data -- prediction of secondary and tertiary protein structures; gene expression data analysis; bio-medical text mining and imaging -- methods for diagnosis and prognosis; mathematical modelling and simulation of biological systems; and intelligent clinical decision support systems (i-CDSS).
出版日期Conference proceedings 2011
關(guān)鍵詞algorithmic learning; computational biology; learning classifier systems; machine learning; micorarray d
版次1
doihttps://doi.org/10.1007/978-3-642-21946-7
isbn_softcover978-3-642-21945-0
isbn_ebook978-3-642-21946-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2011
The information of publication is updating

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De Novo Protein Subcellular Localization Prediction by N-to-1 Neural Networksather by adaptively compressing the sequence into a hidden feature vector. We benchmark SCL_pred against other publicly available predictors using two benchmarks including a new subset of Swiss-Prot release 57. We show that SCL_pred compares favourably to the other state-of-the-art predictors. Moreo
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Osmoprotectants in the Sugarcane (, spp.) Transcriptome Revealed by in Silico Evaluationlysis between the ORFs of sugarcane and other organisms, the genic structure of these plants was relatively conserved suggesting that the accumulation of compatible solutes is an ancient metabolic adaptation.
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Biclustering by Resamplingits to find find several biclusters by using the statistical method of Bootstrap aggregation. We applied the algorithm to a synthetic data and to the Yeast dataset, obtaining fast convergence and good quality solutions. A comparison with original PBC method is also presented.
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Dynamic Simulations of Pathways Downstream of ERBB-Family: Exploration of Parameter Space and Effectd an important sensitivity, the number of sensitive cases increased moderately for increasing numbers of perturbations. In most cases the effect of introducing virtual mutations and virtual onco-protein inhibitors was more important than the effect of randomly introduced perturbations, this suggests
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0302-9743 n structures; gene expression data analysis; bio-medical text mining and imaging -- methods for diagnosis and prognosis; mathematical modelling and simulation of biological systems; and intelligent clinical decision support systems (i-CDSS).978-3-642-21945-0978-3-642-21946-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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