{"product_id":"algorithmic-and-artificial-intelligence-methods-for-protein-bioinformatics-isbn-9781118345788","title":"Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics","description":"Algorithmic and Artificial Intelligence Methods for \u003cb\u003eProtein Bioinformatics\u003c\/b\u003e \u003cp\u003eAn in-depth look at the latest research, methods, and applications in the field of protein bioinformatics \u003c\/p\u003e\u003cp\u003eThis book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, \u003ci\u003eAlgorithmic and Artificial Intelligence Methods for Protein Bioinformatics\u003c\/i\u003e addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. \u003c\/p\u003e\u003cp\u003eFeaturing a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. \u003ci\u003eAlgorithmic and Artificial Intelligence Methods for Protein Bioinformatics:\u003c\/i\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eHighlights protein analysis applications such as protein-related drug activity comparison\u003c\/li\u003e \u003cli\u003eIncorporates salient case studies illustrating how to apply the methods outlined in the book\u003c\/li\u003e \u003cli\u003eTackles the complex relationship between proteins from a systems biology point of view\u003c\/li\u003e \u003cli\u003eRelates the topic to other emerging technologies such as data mining and visualization\u003c\/li\u003e \u003cli\u003eIncludes many tables and illustrations demonstrating concepts and performance figures\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAlgorithmic and Artificial Intelligence Methods for Protein Bioinformatics\u003c\/i\u003e is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.  \u003c\/p\u003e\u003cp\u003ePREFACE ix\u003c\/p\u003e \u003cp\u003eCONTRIBUTORS xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI FROM PROTEIN SEQUENCE TO STRUCTURE\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 EMPHASIZING THE ROLE OF PROTEINS IN CONSTRUCTION OF THE DEVELOPMENTAL GENETIC TOOLKIT IN PLANTS 3\u003cbr\u003e \u003ci\u003eAnamika Basu and Anasua Sarkar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 PROTEIN SEQUENCE MOTIF INFORMATION DISCOVERY 41\u003cbr\u003e \u003ci\u003eBernard Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 IDENTIFYING CALCIUM BINDING SITES IN PROTEINS 57\u003cbr\u003e \u003ci\u003eHui Liu and Hai Deng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 REVIEW OF IMBALANCED DATA LEARNING FOR PROTEIN METHYLATION PREDICTION 71\u003cbr\u003e \u003ci\u003eZejin Ding and Yan-Qing Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 ANALYSIS AND PREDICTION OF PROTEIN POSTTRANSLATIONAL MODIFICATION SITES 91\u003cbr\u003e \u003ci\u003eJianjiong Gao, Qiuming Yao, Curtis Harrison Bollinger, and Dong Xu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII PROTEIN ANALYSIS AND PREDICTION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6 PROTEIN LOCAL STRUCTURE PREDICTION 109\u003cbr\u003e \u003ci\u003eWei Zhong, Jieyue He, Robert W. Harrison, Phang C. Tai, and Yi Pan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 PROTEIN STRUCTURAL BOUNDARY PREDICTION 125\u003cbr\u003e \u003ci\u003eGulsah Altun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 PREDICTION OF RNA BINDING SITES IN PROTEINS 153\u003cbr\u003e \u003ci\u003eZhi-Ping Liu and Luonan Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 ALGORITHMIC FRAMEWORKS FOR PROTEIN DISULFIDE CONNECTIVITY DETERMINATION 171\u003cbr\u003e \u003ci\u003eRahul Singh, William Murad, and Timothy Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 PROTEIN CONTACT ORDER PREDICTION: UPDATE 205\u003cbr\u003e \u003ci\u003eYi Shi, Jianjun Zhou, David S. Wishart, and Guohui Lin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 PROGRESS IN PREDICTION OF OXIDATION STATES OF CYSTEINES VIA COMPUTATIONAL APPROACHES 217\u003cbr\u003e \u003ci\u003eAiguo Du, Hui Liu, Hai Deng, and Yi Pan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 COMPUTATIONAL METHODS IN CRYOELECTRON MICROSCOPY 3D STRUCTURE RECONSTRUCTION 231\u003cbr\u003e \u003ci\u003eFa Zhang, Xiaohua Wan, and Zhiyong Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII PROTEIN STRUCTURE ALIGNMENT AND ASSESSMENT\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13 FUNDAMENTALS OF PROTEIN STRUCTURE ALIGNMENT 255\u003cbr\u003e \u003ci\u003eMark Brandt, Allen Holder, and Yosi Shibberu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 DISCOVERING 3D PROTEIN STRUCTURES FOR OPTIMAL STRUCTURE ALIGNMENT 281\u003cbr\u003e \u003ci\u003eTomáš Novosád, Václav Snášel, Ajith Abraham, and Jack Y. Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 ALGORITHMIC METHODOLOGIES FOR DISCOVERY OF NONSEQUENTIAL PROTEIN STRUCTURE SIMILARITIES 299\u003cbr\u003e Bhaskar DasGupta, Joseph Dundas, and Jie Liang\u003c\/p\u003e \u003cp\u003e16 FRACTAL RELATED METHODS FOR PREDICTING PROTEIN STRUCTURE CLASSES AND FUNCTIONS 317\u003cbr\u003e \u003ci\u003eZu-Guo Yu, Vo Anh, Jian-Yi Yang, and Shao-Ming Zhu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 PROTEIN TERTIARY MODEL ASSESSMENT 339\u003cbr\u003e \u003ci\u003eAnjum Chida, Robert W. Harrison, and Yan-Qing Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV PROTEIN–PROTEIN ANALYSIS OF BIOLOGICAL NETWORKS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18 NETWORK ALGORITHMS FOR PROTEIN INTERACTIONS 357\u003cbr\u003e \u003ci\u003eSuely Oliveira\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19 IDENTIFYING PROTEIN COMPLEXES FROM PROTEIN–PROTEIN INTERACTION NETWORKS 377\u003cbr\u003e \u003ci\u003eJianxin Wang, Min Li, and Xiaoqing Peng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20 PROTEIN FUNCTIONAL MODULE ANALYSIS WITH PROTEIN–PROTEIN INTERACTION (PPI) NETWORKS 393\u003cbr\u003e \u003ci\u003eLei Shi, Xiujuan Lei, and Aidong Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21 EFFICIENT ALIGNMENTS OF METABOLIC NETWORKS WITH BOUNDED TREEWIDTH 413\u003cbr\u003e \u003ci\u003eQiong Cheng, Piotr Berman, Robert W. Harrison, and Alexander Zelikovsky\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22 PROTEIN–PROTEIN INTERACTION NETWORK ALIGNMENT: ALGORITHMS AND TOOLS 431\u003cbr\u003e \u003ci\u003eValeria Fionda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV APPLICATION OF PROTEIN BIOINFORMATICS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23 PROTEIN-RELATED DRUG ACTIVITY COMPARISON USING SUPPORT VECTOR MACHINES 451\u003cbr\u003e \u003ci\u003eWei Zhong and Jieyue He\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24 FINDING REPETITIONS IN BIOLOGICAL NETWORKS: CHALLENGES, TRENDS, AND APPLICATIONS 461\u003cbr\u003e \u003ci\u003eSimona E. Rombo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25 MeTaDoR: ONLINE RESOURCE AND PREDICTION SERVER FOR MEMBRANE TARGETING PERIPHERAL PROTEINS 481\u003cbr\u003e \u003ci\u003eNitin Bhardwaj, Morten Källberg, Wonhwa Cho, and Hui Lu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26 BIOLOGICAL NETWORKS–BASED ANALYSIS OF GENE EXPRESSION SIGNATURES 495\u003cbr\u003e \u003ci\u003eGang Chen and Jianxin Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eINDEX 507\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eYI PAN, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is the Chair and Full Professor in the Department of Computer Science at Georgia State University, and a Visiting Chair Professor in the School of Information Science and Engineering at Central South University in Changsha, China. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMIN LI, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Associate Professor in the School of Information Science and Engineering and a postdoctoral associate in the State Key Laboratory of Medical Genetics at Central South University in Changsha, China. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJIANXIN WANG, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Associate Dean and Full Professor in the School of Information Science and Engineering at Central South University in Changsha, China.   \u003c\/p\u003e\u003cp\u003eAn in-depth look at the latest research, methods, and applications in the field of protein bioinformatics \u003c\/p\u003e\u003cp\u003eThis book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, \u003ci\u003eAlgorithmic and Artificial Intelligence Methods for Protein Bioinformatics\u003c\/i\u003e addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. \u003c\/p\u003e\u003cp\u003eFeaturing a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. \u003ci\u003eAlgorithmic and Artificial Intelligence Methods for Protein Bioinformatics:\u003c\/i\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eHighlights protein analysis applications such as protein-related drug activity comparison\u003c\/li\u003e \u003cli\u003eIncorporates salient case studies illustrating how to apply the methods outlined in the book\u003c\/li\u003e \u003cli\u003eTackles the complex relationship between proteins from a systems biology point of view\u003c\/li\u003e \u003cli\u003eRelates the topic to other emerging technologies such as data mining and visualization\u003c\/li\u003e \u003cli\u003eIncludes many tables and illustrations demonstrating concepts and performance figures\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAlgorithmic and Artificial Intelligence Methods for Protein Bioinformatics\u003c\/i\u003e is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.\u003c\/p\u003e","brand":"Wiley-IEEE Computer Society Pr","offers":[{"title":"Default Title","offer_id":47988713160933,"sku":"NP9781118345788","price":138.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118345788.jpg?v=1761781295","url":"https:\/\/k12savings.com\/products\/algorithmic-and-artificial-intelligence-methods-for-protein-bioinformatics-isbn-9781118345788","provider":"K12savings","version":"1.0","type":"link"}