Details

Biological Knowledge Discovery Handbook


Biological Knowledge Discovery Handbook

Preprocessing, Mining and Postprocessing of Biological Data
Wiley Series in Bioinformatics 1. Aufl.

von: Mourad Elloumi, Albert Y. Zomaya, Yi Pan

164,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 04.02.2015
ISBN/EAN: 9781118853726
Sprache: englisch
Anzahl Seiten: 1192

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Beschreibungen

<p><b>The first comprehensive overview of preprocessing, mining, and postprocessing of biological data</b></p> <p>Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered.</p> <p>Written by top experts, <i>Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data</i> covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems.</p> <p>BIOLOGICAL DATA PREPROCESSING</p> <ul> <li>Part A: Biological Data Management</li> <li>Part B: Biological Data Modeling</li> <li>Part C: Biological Feature Extraction</li> <li>Part D Biological Feature Selection</li> </ul> <p>BIOLOGICAL DATA MINING</p> <ul> <li>Part E: Regression Analysis of Biological Data</li> <li>Part F Biological Data Clustering</li> <li>Part G: Biological Data Classification</li> <li>Part H: Association Rules Learning from Biological Data</li> <li>Part I: Text Mining and Application to Biological Data</li> <li>Part J: High-Performance Computing for Biological Data Mining</li> </ul> <p>Combining sound theory with practical applications in molecular biology, <i>Biological Knowledge Discovery Handbook</i> is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.</p>
<p>PREFACE xiii</p> <p>CONTRIBUTORS xv</p> <p><b>SECTION I BIOLOGICAL DATA PREPROCESSING</b><br /> <br /> <b>PART A: BIOLOGICAL DATA MANAGEMENT</b></p> <p>1 GENOME AND TRANSCRIPTOME SEQUENCE DATABASES FOR DISCOVERY, STORAGE, AND REPRESENTATION OF ALTERNATIVE SPLICING EVENTS 5<br /> <i>Bahar Taneri and Terry Gaasterland</i></p> <p>2 CLEANING, INTEGRATING, AND WAREHOUSING GENOMIC DATA FROM BIOMEDICAL RESOURCES 35<br /> <i>Fouzia Moussouni and Laure Berti-Equille</i></p> <p>3 CLEANSING OF MASS SPECTROMETRY DATA FOR PROTEIN IDENTIFICATION AND QUANTIFICATION 59<br /> <i>Penghao Wang and Albert Y. Zomaya</i></p> <p>4 FILTERING PROTEIN–PROTEIN INTERACTIONS BY INTEGRATION OF ONTOLOGY DATA 77<br /> <i>Young-Rae Cho</i></p> <p><b>PART B: BIOLOGICAL DATA MODELING</b></p> <p>5 COMPLEXITY AND SYMMETRIES IN DNA SEQUENCES 95<br /> <i>Carlo Cattani</i></p> <p>6 ONTOLOGY-DRIVEN FORMAL CONCEPTUAL DATA MODELING FOR BIOLOGICAL DATA ANALYSIS 129<br /> <i>Catharina Maria Keet</i></p> <p>7 BIOLOGICAL DATA INTEGRATION USING NETWORK MODELS 155<br /> <i>Gaurav Kumar and Shoba Ranganathan</i></p> <p>8 NETWORK MODELING OF STATISTICAL EPISTASIS 175<br /> <i>Ting Hu and Jason H. Moore</i></p> <p>9 GRAPHICAL MODELS FOR PROTEIN FUNCTION AND STRUCTURE PREDICTION 191<br /> <i>Mingjie Tang, Kean Ming Tan, Xin Lu Tan, Lee Sael, Meghana Chitale, Juan Esquivel-Rodrýguez, and Daisuke Kihara</i></p> <p><b>PART C: BIOLOGICAL FEATURE EXTRACTION</b></p> <p>10 ALGORITHMS AND DATA STRUCTURES FOR NEXT-GENERATION SEQUENCES 225<br /> <i>Francesco Vezzi, Giuseppe Lancia, and Alberto Policriti</i></p> <p>11 ALGORITHMS FOR NEXT-GENERATION SEQUENCING DATA 251<br /> <i>Costas S. Iliopoulos and Solon P. Pissis</i></p> <p>12 GENE REGULATORY NETWORK IDENTIFICATION WITH QUALITATIVE PROBABILISTIC NETWORKS 281<br /> <i>Zina M. Ibrahim, Alioune Ngom, and Ahmed Y. Tawfik</i></p> <p><b>PART D: BIOLOGICAL FEATURE SELECTION</b></p> <p>13 COMPARING, RANKING, AND FILTERING MOTIFS WITH<br /> CHARACTER CLASSES: APPLICATION TO BIOLOGICAL SEQUENCES ANALYSIS 309<br /> <i>Matteo Comin and Davide Verzotto</i></p> <p>14 STABILITY OF FEATURE SELECTION ALGORITHMS AND ENSEMBLE FEATURE SELECTION METHODS IN<br /> BIOINFORMATICS 333<br /> <i>Pengyi Yang, Bing B. Zhou, Jean Yee-Hwa Yang, and Albert Y. Zomaya</i></p> <p>15 STATISTICAL SIGNIFICANCE ASSESSMENT FOR BIOLOGICAL FEATURE SELECTION: METHODS AND ISSUES 353<br /> <i>Juntao Li, Kwok Pui Choi, Yudi Pawitan, and Radha Krishna Murthy Karuturi</i></p> <p>16 SURVEY OF NOVEL FEATURE SELECTION METHODS FOR CANCER CLASSIFICATION 379<br /> <i>Oleg Okun</i></p> <p>17 INFORMATION-THEORETIC GENE SELECTION IN EXPRESSION DATA 399<br /> <i>Patrick E. Meyer and Gianluca Bontempi</i></p> <p>18 FEATURE SELECTION AND CLASSIFICATION FOR GENE EXPRESSION DATA USING EVOLUTIONARY COMPUTATION 421<br /> <i>Haider Banka, Suresh Dara, and Mourad Elloumi</i></p> <p><b>SECTION II BIOLOGICAL DATA MINING</b></p> <p><b>PART E: REGRESSION ANALYSIS OF BIOLOGICAL DATA</b></p> <p>19 BUILDING VALID REGRESSION MODELS FOR BIOLOGICAL DATA USING STATA AND R 445<br /> <i>Charles Lindsey and Simon J. Sheather</i></p> <p>20 LOGISTIC REGRESSION IN GENOMEWIDE ASSOCIATION ANALYSIS 477<br /> <i>Wentian Li and Yaning Yang</i></p> <p>21 SEMIPARAMETRIC REGRESSION METHODS IN LONGITUDINAL DATA: APPLICATIONS TO AIDS CLINICAL TRIAL DATA 501<br /> <i>Yehua Li</i></p> <p><b>PART F: BIOLOGICAL DATA CLUSTERING</b></p> <p>22 THE THREE STEPS OF CLUSTERING IN THE POST-GENOMIC ERA 521<br /> <i>Raffaele Giancarlo, Giosu´e Lo Bosco, Luca Pinello, and Filippo Utro</i></p> <p>23 CLUSTERING ALGORITHMS OF MICROARRAY DATA 557<br /> <i>Haifa Ben Saber, Mourad Elloumi, and Mohamed Nadif</i></p> <p>24 SPREAD OF EVALUATION MEASURES FOR MICROARRAY CLUSTERING 569<br /> <i>Giulia Bruno and Alessandro Fiori</i></p> <p>25 SURVEY ON BICLUSTERING OF GENE EXPRESSION DATA 591<br /> <i>Adelaide Valente Freitas, Wassim Ayadi, Mourad Elloumi, Jose Luis Oliveira, and Jin-Kao Hao</i></p> <p>26 MULTIOBJECTIVE BICLUSTERING OF GENE EXPRESSION DATA WITH BIOINSPIRED ALGORITHMS 609<br /> <i>Khedidja Seridi, Laetitia Jourdan, and El-Ghazali Talbi</i></p> <p>27 COCLUSTERING UNDER GENE ONTOLOGY DERIVED CONSTRAINTS FOR PATHWAY IDENTIFICATION 625<br /> <i>Alessia Visconti, Francesca Cordero, Dino Ienco, and Ruggero G. Pensa</i></p> <p><b>PART G: BIOLOGICAL DATA CLASSIFICATION</b></p> <p>28 SURVEY ON FINGERPRINT CLASSIFICATION METHODS FOR BIOLOGICAL SEQUENCES 645<br /> <i>Bhaskar DasGupta and Lakshmi Kaligounder</i></p> <p>29 MICROARRAY DATA ANALYSIS: FROM PREPARATION TO CLASSIFICATION 657<br /> <i>Luciano Cascione, Alfredo Ferro, Rosalba Giugno, Giuseppe Pigola, and Alfredo Pulvirenti</i></p> <p>30 DIVERSIFIED CLASSIFIER FUSION TECHNIQUE FOR GENE EXPRESSION DATA 675<br /> <i>Sashikala Mishra, Kailash Shaw, and Debahuti Mishra</i></p> <p>31 RNA CLASSIFICATION AND STRUCTURE PREDICTION: ALGORITHMS AND CASE STUDIES 685<br /> <i>Ling Zhong, Junilda Spirollari, Jason T. L. Wang, and Dongrong Wen</i></p> <p>32 AB INITIO PROTEIN STRUCTURE PREDICTION: METHODS AND CHALLENGES 703<br /> <i>Jad Abbass, Jean-Christophe Nebel, and Nashat Mansour</i></p> <p>33 OVERVIEW OF CLASSIFICATION METHODS TO<br /> SUPPORT HIV/AIDS CLINICAL DECISION MAKING 725<br /> <i>Khairul A. Kasmiran, Ali Al Mazari, Albert Y. Zomaya, and Roger J. Garsia</i></p> <p><b>PART H: ASSOCIATION RULES LEARNING FROM BIOLOGICAL DATA</b></p> <p>34 MINING FREQUENT PATTERNS AND ASSOCIATION RULES FROM BIOLOGICAL DATA 737<br /> <i>Ioannis Kavakiotis, George Tzanis, and Ioannis Vlahavas</i></p> <p>35 GALOIS CLOSURE BASED ASSOCIATION RULE MINING FROM BIOLOGICAL DATA 761<br /> <i>Kartick Chandra Mondal and Nicolas Pasquier</i></p> <p>36 INFERENCE OF GENE REGULATORY NETWORKS BASED ON ASSOCIATION RULES 803<br /> <i>Cristian Andres Gallo, Jessica Andrea Carballido, and Ignacio Ponzoni</i></p> <p><b>PART I: TEXT MINING AND APPLICATION TO BIOLOGICAL DATA</b></p> <p>37 CURRENT METHODOLOGIES FOR BIOMEDICAL NAMED ENTITY RECOGNITION 841<br /> <i>David Campos, Sergio Matos, and José Luýs Oliveira</i></p> <p>38 AUTOMATED ANNOTATION OF SCIENTIFIC DOCUMENTS: INCREASING ACCESS TO BIOLOGICAL KNOWLEDGE 869<br /> <i>Evangelos Pafilis, Heiko Horn, and Nigel P. Brown</i></p> <p>39 AUGMENTING BIOLOGICAL TEXT MINING WITH SYMBOLIC INFERENCE 901<br /> <i>Jong C. Park and Hee-Jin Lee</i></p> <p>40 WEB CONTENT MINING FOR LEARNING GENERIC RELATIONS AND THEIR ASSOCIATIONS FROM TEXTUAL BIOLOGICAL DATA 919<br /> <i>Muhammad Abulaish and Jahiruddin</i></p> <p>41 PROTEIN–PROTEIN RELATION EXTRACTION FROM BIOMEDICAL ABSTRACTS 943<br /> <i>Syed Toufeeq Ahmed, Hasan Davulcu, Sukru Tikves, Radhika Nair, and Chintan Patel</i></p> <p><b>PART J: HIGH-PERFORMANCE COMPUTING FOR BIOLOGICAL DATA MINING</b></p> <p>42 ACCELERATING PAIRWISE ALIGNMENT ALGORITHMS BY USING GRAPHICS PROCESSOR UNITS 971<br /> <i>Mourad Elloumi, Mohamed Al Sayed Issa, and Ahmed Mokaddem</i></p> <p>43 HIGH-PERFORMANCE COMPUTING IN HIGH-THROUGHPUT SEQUENCING 981<br /> <i>Kamer Kaya, Ayat Hatem, Hatice Gulcin Ozer, Kun Huang, and Umit V. Catalyurek</i></p> <p>44 LARGE-SCALE CLUSTERING OF SHORT READS FOR METAGENOMICS ON GPUs 1003<br /> <i>Thuy Diem Nguyen, Bertil Schmidt, Zejun Zheng, and Chee Keong Kwoh</i></p> <p><b>SECTION III BIOLOGICAL DATA POSTPROCESSING</b></p> <p><b>PART K: BIOLOGICAL KNOWLEDGE INTEGRATION AND VISUALIZATION</b></p> <p>45 INTEGRATION OF METABOLIC KNOWLEDGE FOR GENOME-SCALE METABOLIC RECONSTRUCTION 1027<br /> <i>Ali Masoudi-Nejad, Ali Salehzadeh-Yazdi, Shiva Akbari-Birgani, and Yazdan Asgari</i></p> <p>46 INFERRING AND POSTPROCESSING HUGE PHYLOGENIES 1049<br /> <i>Stephen A. Smith and Alexandros Stamatakis</i></p> <p>47 BIOLOGICAL KNOWLEDGE VISUALIZATION 1073<br /> <i>Rodrigo Santamarýa</i></p> <p>48 VISUALIZATION OF BIOLOGICAL KNOWLEDGE BASED ON MULTIMODAL BIOLOGICAL DATA 1109<br /> <i>Hendrik Rohn and Falk Schreiber</i></p> <p>INDEX 1127</p>
<p>“This book is a unique resource for practitioners and researchers in computer science, life science, and  mathematics.”  (<i>Zentralblatt MATH</i>, 1 June 2015)</p> <p> </p>
<p><b>MOURAD ELLOUMI</b> is a Full Professor in Computer Science at the University of Tunis-El Manar, Tunisia. He is the author/coauthor of more than fifty publications in international journals and conference proceedings and the coeditor, along with Albert Zomaya, of <i>Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications</i> (Wiley).</p> <p><b>ALBERT Y. ZOMAYA</b> is the Chair Professor of High Performance Computing & Networking at The University of Sydney's School of Information Technologies. He is the author/coauthor of seven books, more than 450 publications in technical journals and conference proceedings, and the editor of fourteen books and nineteen conference volumes. He is a Fellow of the IEEE, the American Association for the Advancement of Science, and IET (UK).</p>
<p><b>The first comprehensive overview of preprocessing, mining, and postprocessing of biological data</b></p> <p>Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered.</p> <p>Written by top experts, <i>Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data</i> covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems.</p> <p>BIOLOGICAL DATA PREPROCESSING</p> <ul> <li>Part A: Biological Data Management</li> <li>Part B: Biological Data Modeling</li> <li>Part C: Biological Feature Extraction</li> <li>Part D Biological Feature Selection</li> </ul> <p>BIOLOGICAL DATA MINING</p> <ul> <li>Part E: Regression Analysis of Biological Data</li> <li>Part F Biological Data Clustering</li> <li>Part G: Biological Data Classification</li> <li>Part H: Association Rules Learning from Biological Data</li> <li>Part I: Text Mining and Application to Biological Data</li> <li>Part J: High-Performance Computing for Biological Data Mining</li> </ul> <p>Combining sound theory with practical applications in molecular biology, <i>Biological Knowledge Discovery Handbook</i> is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.</p>

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