{"product_id":"biological-knowledge-discovery-handbook-isbn-9781118132739","title":"Biological Knowledge Discovery Handbook","description":"\u003cp\u003e\u003cb\u003eThe first comprehensive overview of preprocessing, mining, and postprocessing of biological data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMolecular 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.\u003c\/p\u003e \u003cp\u003eWritten by top experts, \u003ci\u003eBiological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eBIOLOGICAL DATA PREPROCESSING\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePart A: Biological Data Management\u003c\/li\u003e \u003cli\u003ePart B: Biological Data Modeling\u003c\/li\u003e \u003cli\u003ePart C: Biological Feature Extraction\u003c\/li\u003e \u003cli\u003ePart D Biological Feature Selection\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eBIOLOGICAL DATA MINING\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePart E: Regression Analysis of Biological Data\u003c\/li\u003e \u003cli\u003ePart F Biological Data Clustering\u003c\/li\u003e \u003cli\u003ePart G: Biological Data Classification\u003c\/li\u003e \u003cli\u003ePart H: Association Rules Learning from Biological Data\u003c\/li\u003e \u003cli\u003ePart I: Text Mining and Application to Biological Data\u003c\/li\u003e \u003cli\u003ePart J: High-Performance Computing for Biological Data Mining\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eCombining sound theory with practical applications in molecular biology, \u003ci\u003eBiological Knowledge Discovery Handbook\u003c\/i\u003e is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.\u003c\/p\u003e  \u003cp\u003ePREFACE xiii\u003c\/p\u003e \u003cp\u003eCONTRIBUTORS xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSECTION I BIOLOGICAL DATA PREPROCESSING\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003ePART A: BIOLOGICAL DATA MANAGEMENT\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 GENOME AND TRANSCRIPTOME SEQUENCE DATABASES FOR DISCOVERY, STORAGE, AND REPRESENTATION OF ALTERNATIVE SPLICING EVENTS 5\u003cbr\u003e \u003ci\u003eBahar Taneri and Terry Gaasterland\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 CLEANING, INTEGRATING, AND WAREHOUSING GENOMIC DATA FROM BIOMEDICAL RESOURCES 35\u003cbr\u003e \u003ci\u003eFouzia Moussouni and Laure Berti-Equille\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 CLEANSING OF MASS SPECTROMETRY DATA FOR PROTEIN IDENTIFICATION AND QUANTIFICATION 59\u003cbr\u003e \u003ci\u003ePenghao Wang and Albert Y. Zomaya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 FILTERING PROTEIN–PROTEIN INTERACTIONS BY INTEGRATION OF ONTOLOGY DATA 77\u003cbr\u003e \u003ci\u003eYoung-Rae Cho\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART B: BIOLOGICAL DATA MODELING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5 COMPLEXITY AND SYMMETRIES IN DNA SEQUENCES 95\u003cbr\u003e \u003ci\u003eCarlo Cattani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 ONTOLOGY-DRIVEN FORMAL CONCEPTUAL DATA MODELING FOR BIOLOGICAL DATA ANALYSIS 129\u003cbr\u003e \u003ci\u003eCatharina Maria Keet\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 BIOLOGICAL DATA INTEGRATION USING NETWORK MODELS 155\u003cbr\u003e \u003ci\u003eGaurav Kumar and Shoba Ranganathan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 NETWORK MODELING OF STATISTICAL EPISTASIS 175\u003cbr\u003e \u003ci\u003eTing Hu and Jason H. Moore\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 GRAPHICAL MODELS FOR PROTEIN FUNCTION AND STRUCTURE PREDICTION 191\u003cbr\u003e \u003ci\u003eMingjie Tang, Kean Ming Tan, Xin Lu Tan, Lee Sael, Meghana Chitale, Juan Esquivel-Rodrýguez, and Daisuke Kihara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART C: BIOLOGICAL FEATURE EXTRACTION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10 ALGORITHMS AND DATA STRUCTURES FOR NEXT-GENERATION SEQUENCES 225\u003cbr\u003e \u003ci\u003eFrancesco Vezzi, Giuseppe Lancia, and Alberto Policriti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 ALGORITHMS FOR NEXT-GENERATION SEQUENCING DATA 251\u003cbr\u003e \u003ci\u003eCostas S. Iliopoulos and Solon P. Pissis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 GENE REGULATORY NETWORK IDENTIFICATION WITH QUALITATIVE PROBABILISTIC NETWORKS 281\u003cbr\u003e \u003ci\u003eZina M. Ibrahim, Alioune Ngom, and Ahmed Y. Tawfik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART D: BIOLOGICAL FEATURE SELECTION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13 COMPARING, RANKING, AND FILTERING MOTIFS WITH\u003cbr\u003e CHARACTER CLASSES: APPLICATION TO BIOLOGICAL SEQUENCES ANALYSIS 309\u003cbr\u003e \u003ci\u003eMatteo Comin and Davide Verzotto\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 STABILITY OF FEATURE SELECTION ALGORITHMS AND ENSEMBLE FEATURE SELECTION METHODS IN\u003cbr\u003e BIOINFORMATICS 333\u003cbr\u003e \u003ci\u003ePengyi Yang, Bing B. Zhou, Jean Yee-Hwa Yang, and Albert Y. Zomaya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 STATISTICAL SIGNIFICANCE ASSESSMENT FOR BIOLOGICAL FEATURE SELECTION: METHODS AND ISSUES 353\u003cbr\u003e \u003ci\u003eJuntao Li, Kwok Pui Choi, Yudi Pawitan, and Radha Krishna Murthy Karuturi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 SURVEY OF NOVEL FEATURE SELECTION METHODS FOR CANCER CLASSIFICATION 379\u003cbr\u003e \u003ci\u003eOleg Okun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 INFORMATION-THEORETIC GENE SELECTION IN EXPRESSION DATA 399\u003cbr\u003e \u003ci\u003ePatrick E. Meyer and Gianluca Bontempi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 FEATURE SELECTION AND CLASSIFICATION FOR GENE EXPRESSION DATA USING EVOLUTIONARY COMPUTATION 421\u003cbr\u003e \u003ci\u003eHaider Banka, Suresh Dara, and Mourad Elloumi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSECTION II BIOLOGICAL DATA MINING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART E: REGRESSION ANALYSIS OF BIOLOGICAL DATA\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19 BUILDING VALID REGRESSION MODELS FOR BIOLOGICAL DATA USING STATA AND R 445\u003cbr\u003e \u003ci\u003eCharles Lindsey and Simon J. Sheather\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20 LOGISTIC REGRESSION IN GENOMEWIDE ASSOCIATION ANALYSIS 477\u003cbr\u003e \u003ci\u003eWentian Li and Yaning Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21 SEMIPARAMETRIC REGRESSION METHODS IN LONGITUDINAL DATA: APPLICATIONS TO AIDS CLINICAL TRIAL DATA 501\u003cbr\u003e \u003ci\u003eYehua Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART F: BIOLOGICAL DATA CLUSTERING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22 THE THREE STEPS OF CLUSTERING IN THE POST-GENOMIC ERA 521\u003cbr\u003e \u003ci\u003eRaffaele Giancarlo, Giosu´e Lo Bosco, Luca Pinello, and Filippo Utro\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23 CLUSTERING ALGORITHMS OF MICROARRAY DATA 557\u003cbr\u003e \u003ci\u003eHaifa Ben Saber, Mourad Elloumi, and Mohamed Nadif\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24 SPREAD OF EVALUATION MEASURES FOR MICROARRAY CLUSTERING 569\u003cbr\u003e \u003ci\u003eGiulia Bruno and Alessandro Fiori\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25 SURVEY ON BICLUSTERING OF GENE EXPRESSION DATA 591\u003cbr\u003e \u003ci\u003eAdelaide Valente Freitas, Wassim Ayadi, Mourad Elloumi, Jose Luis Oliveira, and Jin-Kao Hao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26 MULTIOBJECTIVE BICLUSTERING OF GENE EXPRESSION DATA WITH BIOINSPIRED ALGORITHMS 609\u003cbr\u003e \u003ci\u003eKhedidja Seridi, Laetitia Jourdan, and El-Ghazali Talbi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27 COCLUSTERING UNDER GENE ONTOLOGY DERIVED CONSTRAINTS FOR PATHWAY IDENTIFICATION 625\u003cbr\u003e \u003ci\u003eAlessia Visconti, Francesca Cordero, Dino Ienco, and Ruggero G. Pensa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART G: BIOLOGICAL DATA CLASSIFICATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28 SURVEY ON FINGERPRINT CLASSIFICATION METHODS FOR BIOLOGICAL SEQUENCES 645\u003cbr\u003e \u003ci\u003eBhaskar DasGupta and Lakshmi Kaligounder\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29 MICROARRAY DATA ANALYSIS: FROM PREPARATION TO CLASSIFICATION 657\u003cbr\u003e \u003ci\u003eLuciano Cascione, Alfredo Ferro, Rosalba Giugno, Giuseppe Pigola, and Alfredo Pulvirenti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30 DIVERSIFIED CLASSIFIER FUSION TECHNIQUE FOR GENE EXPRESSION DATA 675\u003cbr\u003e \u003ci\u003eSashikala Mishra, Kailash Shaw, and Debahuti Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e31 RNA CLASSIFICATION AND STRUCTURE PREDICTION: ALGORITHMS AND CASE STUDIES 685\u003cbr\u003e \u003ci\u003eLing Zhong, Junilda Spirollari, Jason T. L. Wang, and Dongrong Wen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e32 AB INITIO PROTEIN STRUCTURE PREDICTION: METHODS AND CHALLENGES 703\u003cbr\u003e \u003ci\u003eJad Abbass, Jean-Christophe Nebel, and Nashat Mansour\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e33 OVERVIEW OF CLASSIFICATION METHODS TO\u003cbr\u003e SUPPORT HIV\/AIDS CLINICAL DECISION MAKING 725\u003cbr\u003e \u003ci\u003eKhairul A. Kasmiran, Ali Al Mazari, Albert Y. Zomaya, and Roger J. Garsia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART H: ASSOCIATION RULES LEARNING FROM BIOLOGICAL DATA\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e34 MINING FREQUENT PATTERNS AND ASSOCIATION RULES FROM BIOLOGICAL DATA 737\u003cbr\u003e \u003ci\u003eIoannis Kavakiotis, George Tzanis, and Ioannis Vlahavas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e35 GALOIS CLOSURE BASED ASSOCIATION RULE MINING FROM BIOLOGICAL DATA 761\u003cbr\u003e \u003ci\u003eKartick Chandra Mondal and Nicolas Pasquier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e36 INFERENCE OF GENE REGULATORY NETWORKS BASED ON ASSOCIATION RULES 803\u003cbr\u003e \u003ci\u003eCristian Andres Gallo, Jessica Andrea Carballido, and Ignacio Ponzoni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I: TEXT MINING AND APPLICATION TO BIOLOGICAL DATA\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e37 CURRENT METHODOLOGIES FOR BIOMEDICAL NAMED ENTITY RECOGNITION 841\u003cbr\u003e \u003ci\u003eDavid Campos, Sergio Matos, and José Luýs Oliveira\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e38 AUTOMATED ANNOTATION OF SCIENTIFIC DOCUMENTS: INCREASING ACCESS TO BIOLOGICAL KNOWLEDGE 869\u003cbr\u003e \u003ci\u003eEvangelos Pafilis, Heiko Horn, and Nigel P. Brown\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e39 AUGMENTING BIOLOGICAL TEXT MINING WITH SYMBOLIC INFERENCE 901\u003cbr\u003e \u003ci\u003eJong C. Park and Hee-Jin Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e40 WEB CONTENT MINING FOR LEARNING GENERIC RELATIONS AND THEIR ASSOCIATIONS FROM TEXTUAL BIOLOGICAL DATA 919\u003cbr\u003e \u003ci\u003eMuhammad Abulaish and Jahiruddin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e41 PROTEIN–PROTEIN RELATION EXTRACTION FROM BIOMEDICAL ABSTRACTS 943\u003cbr\u003e \u003ci\u003eSyed Toufeeq Ahmed, Hasan Davulcu, Sukru Tikves, Radhika Nair, and Chintan Patel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART J: HIGH-PERFORMANCE COMPUTING FOR BIOLOGICAL DATA MINING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e42 ACCELERATING PAIRWISE ALIGNMENT ALGORITHMS BY USING GRAPHICS PROCESSOR UNITS 971\u003cbr\u003e \u003ci\u003eMourad Elloumi, Mohamed Al Sayed Issa, and Ahmed Mokaddem\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e43 HIGH-PERFORMANCE COMPUTING IN HIGH-THROUGHPUT SEQUENCING 981\u003cbr\u003e \u003ci\u003eKamer Kaya, Ayat Hatem, Hatice Gulcin Ozer, Kun Huang, and Umit V. Catalyurek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e44 LARGE-SCALE CLUSTERING OF SHORT READS FOR METAGENOMICS ON GPUs 1003\u003cbr\u003e \u003ci\u003eThuy Diem Nguyen, Bertil Schmidt, Zejun Zheng, and Chee Keong Kwoh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSECTION III BIOLOGICAL DATA POSTPROCESSING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART K: BIOLOGICAL KNOWLEDGE INTEGRATION AND VISUALIZATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e45 INTEGRATION OF METABOLIC KNOWLEDGE FOR GENOME-SCALE METABOLIC RECONSTRUCTION 1027\u003cbr\u003e \u003ci\u003eAli Masoudi-Nejad, Ali Salehzadeh-Yazdi, Shiva Akbari-Birgani, and Yazdan Asgari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e46 INFERRING AND POSTPROCESSING HUGE PHYLOGENIES 1049\u003cbr\u003e \u003ci\u003eStephen A. Smith and Alexandros Stamatakis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e47 BIOLOGICAL KNOWLEDGE VISUALIZATION 1073\u003cbr\u003e \u003ci\u003eRodrigo Santamarýa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e48 VISUALIZATION OF BIOLOGICAL KNOWLEDGE BASED ON MULTIMODAL BIOLOGICAL DATA 1109\u003cbr\u003e \u003ci\u003eHendrik Rohn and Falk Schreiber\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eINDEX 1127\u003c\/p\u003e \u003cp\u003e“This book is a unique resource for practitioners and researchers in computer science, life science, and  mathematics.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 June 2015)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eMOURAD ELLOUMI\u003c\/b\u003e 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 \u003ci\u003eAlgorithms in Computational Molecular Biology: Techniques, Approaches and Applications\u003c\/i\u003e (Wiley).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eALBERT Y. ZOMAYA\u003c\/b\u003e is the Chair Professor of High Performance Computing \u0026amp; 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).\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eThe first comprehensive overview of preprocessing, mining, and postprocessing of biological data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMolecular 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.\u003c\/p\u003e \u003cp\u003eWritten by top experts, \u003ci\u003eBiological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eBIOLOGICAL DATA PREPROCESSING\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePart A: Biological Data Management\u003c\/li\u003e \u003cli\u003ePart B: Biological Data Modeling\u003c\/li\u003e \u003cli\u003ePart C: Biological Feature Extraction\u003c\/li\u003e \u003cli\u003ePart D Biological Feature Selection\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eBIOLOGICAL DATA MINING\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePart E: Regression Analysis of Biological Data\u003c\/li\u003e \u003cli\u003ePart F Biological Data Clustering\u003c\/li\u003e \u003cli\u003ePart G: Biological Data Classification\u003c\/li\u003e \u003cli\u003ePart H: Association Rules Learning from Biological Data\u003c\/li\u003e \u003cli\u003ePart I: Text Mining and Application to Biological Data\u003c\/li\u003e \u003cli\u003ePart J: High-Performance Computing for Biological Data Mining\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eCombining sound theory with practical applications in molecular biology, \u003ci\u003eBiological Knowledge Discovery Handbook\u003c\/i\u003e is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988823687397,"sku":"NP9781118132739","price":209.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118132739.jpg?v=1761781713","url":"https:\/\/k12savings.com\/es\/products\/biological-knowledge-discovery-handbook-isbn-9781118132739","provider":"K12savings","version":"1.0","type":"link"}