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Computational Intelligence Methods for Bioinformatics and Biostatistics

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Cover of 'Computational Intelligence Methods for Bioinformatics and Biostatistics'

Table of Contents

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    Book Overview
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    Chapter 1 Management and Analysis of Protein-to-Protein Interaction Data
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    Chapter 2 The Three Steps of Clustering in the Post-Genomic Era: A Synopsis
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    Chapter 3 Computational Intelligence Methods for Bioinformatics and Biostatistics
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    Chapter 4 Osmoprotectants in the Sugarcane ( Saccharum spp.) Transcriptome Revealed by in Silico Evaluation
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    Chapter 5 IP6K Gene Discovery in Plant mtDNA
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    Chapter 6 Identification and Expression of Early Nodulin in Sugarcane Transcriptome Revealed by in Silico Analysis
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    Chapter 7 An Interactive Method of Anatomical Segmentation and Gene Expression Estimation for an Experimental Mouse Brain Slice
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    Chapter 8 Prediction of the Bonding State of Cysteine Residues in Proteins with Machine-Learning Methods
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    Chapter 9 Supervised Classification Methods for Mining Cell Differences as Depicted by Raman Spectroscopy
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    Chapter 10 Use of Biplots and Partial Least Squares Regression in Microarray Data Analysis for Assessing Association between Genes Involved in Different Biological Pathways
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    Chapter 11 Qualitative Reasoning on Systematic Gene Perturbation Experiments
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    Chapter 12 Biclustering by Resampling
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    Chapter 13 Labeling Negative Examples in Supervised Learning of New Gene Regulatory Connections
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    Chapter 14 MOSCFRA: A Multi-objective Genetic Approach for Simultaneous Clustering and Gene Ranking
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    Chapter 15 A Multi-relational Learning Framework to Support Biomedical Applications
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    Chapter 16 Data Driven Generation of Fuzzy Systems: An Application to Breast Cancer Detection
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    Chapter 17 A Knowledge Based Decision Support System for Bioinformatics and System Biology
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    Chapter 18 Dynamic Simulations of Pathways Downstream of ERBB-Family: Exploration of Parameter Space and Effects of Its Variation on Network Behavior
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    Chapter 19 Robustness Analysis of a Linear Dynamical Model of the Drosophila Gene Expression
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    Chapter 20 Intelligent Clinical Decision Support Systems for Non-invasive Bladder Cancer Diagnosis
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    Chapter 21 Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering
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    Chapter 22 Classification of Clinical Gene-Sample-Time Microarray Expression Data via Tensor Decomposition Methods
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Computational Intelligence Methods for Bioinformatics and Biostatistics
Published by
Springer Science & Business Media, July 2011
DOI 10.1007/978-3-642-21946-7
978-3-64-221945-0, 978-3-64-221946-7

Rizzo, Riccardo, Lisboa, Paulo J. G., Rizzo, Riccardo, Lisboa, Paulo J. G.

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 22%
Student > Ph. D. Student 1 11%
Librarian 1 11%
Researcher 1 11%
Student > Postgraduate 1 11%
Other 0 0%
Unknown 3 33%
Readers by discipline Count As %
Computer Science 2 22%
Biochemistry, Genetics and Molecular Biology 1 11%
Mathematics 1 11%
Agricultural and Biological Sciences 1 11%
Engineering 1 11%
Other 0 0%
Unknown 3 33%