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<p>PREFACE ix</p> <p>CONTRIBUTORS xv</p> <p><b>I FROM PROTEIN SEQUENCE TO STRUCTURE</b></p> <p>1 EMPHASIZING THE ROLE OF PROTEINS IN CONSTRUCTION OF THE DEVELOPMENTAL GENETIC TOOLKIT IN PLANTS 3<br /> <i>Anamika Basu and Anasua Sarkar</i></p> <p>2 PROTEIN SEQUENCE MOTIF INFORMATION DISCOVERY 41<br /> <i>Bernard Chen</i></p> <p>3 IDENTIFYING CALCIUM BINDING SITES IN PROTEINS 57<br /> <i>Hui Liu and Hai Deng</i></p> <p>4 REVIEW OF IMBALANCED DATA LEARNING FOR PROTEIN METHYLATION PREDICTION 71<br /> <i>Zejin Ding and Yan-Qing Zhang</i></p> <p>5 ANALYSIS AND PREDICTION OF PROTEIN POSTTRANSLATIONAL MODIFICATION SITES 91<br /> <i>Jianjiong Gao, Qiuming Yao, Curtis Harrison Bollinger, and Dong Xu</i></p> <p><b>II PROTEIN ANALYSIS AND PREDICTION</b></p> <p>6 PROTEIN LOCAL STRUCTURE PREDICTION 109<br /> <i>Wei Zhong, Jieyue He, Robert W. Harrison, Phang C. Tai, and Yi Pan</i></p> <p>7 PROTEIN STRUCTURAL BOUNDARY PREDICTION 125<br /> <i>Gulsah Altun</i></p> <p>8 PREDICTION OF RNA BINDING SITES IN PROTEINS 153<br /> <i>Zhi-Ping Liu and Luonan Chen</i></p> <p>9 ALGORITHMIC FRAMEWORKS FOR PROTEIN DISULFIDE CONNECTIVITY DETERMINATION 171<br /> <i>Rahul Singh, William Murad, and Timothy Lee</i></p> <p>10 PROTEIN CONTACT ORDER PREDICTION: UPDATE 205<br /> <i>Yi Shi, Jianjun Zhou, David S. Wishart, and Guohui Lin</i></p> <p>11 PROGRESS IN PREDICTION OF OXIDATION STATES OF CYSTEINES VIA COMPUTATIONAL APPROACHES 217<br /> <i>Aiguo Du, Hui Liu, Hai Deng, and Yi Pan</i></p> <p>12 COMPUTATIONAL METHODS IN CRYOELECTRON MICROSCOPY 3D STRUCTURE RECONSTRUCTION 231<br /> <i>Fa Zhang, Xiaohua Wan, and Zhiyong Liu</i></p> <p><b>III PROTEIN STRUCTURE ALIGNMENT AND ASSESSMENT</b></p> <p>13 FUNDAMENTALS OF PROTEIN STRUCTURE ALIGNMENT 255<br /> <i>Mark Brandt, Allen Holder, and Yosi Shibberu</i></p> <p>14 DISCOVERING 3D PROTEIN STRUCTURES FOR OPTIMAL STRUCTURE ALIGNMENT 281<br /> <i>Tomáš Novosád, Václav Snášel, Ajith Abraham, and Jack Y. Yang</i></p> <p>15 ALGORITHMIC METHODOLOGIES FOR DISCOVERY OF NONSEQUENTIAL PROTEIN STRUCTURE SIMILARITIES 299<br /> Bhaskar DasGupta, Joseph Dundas, and Jie Liang</p> <p>16 FRACTAL RELATED METHODS FOR PREDICTING PROTEIN STRUCTURE CLASSES AND FUNCTIONS 317<br /> <i>Zu-Guo Yu, Vo Anh, Jian-Yi Yang, and Shao-Ming Zhu</i></p> <p>17 PROTEIN TERTIARY MODEL ASSESSMENT 339<br /> <i>Anjum Chida, Robert W. Harrison, and Yan-Qing Zhang</i></p> <p><b>IV PROTEIN–PROTEIN ANALYSIS OF BIOLOGICAL NETWORKS</b></p> <p>18 NETWORK ALGORITHMS FOR PROTEIN INTERACTIONS 357<br /> <i>Suely Oliveira</i></p> <p>19 IDENTIFYING PROTEIN COMPLEXES FROM PROTEIN–PROTEIN INTERACTION NETWORKS 377<br /> <i>Jianxin Wang, Min Li, and Xiaoqing Peng</i></p> <p>20 PROTEIN FUNCTIONAL MODULE ANALYSIS WITH PROTEIN–PROTEIN INTERACTION (PPI) NETWORKS 393<br /> <i>Lei Shi, Xiujuan Lei, and Aidong Zhang</i></p> <p>21 EFFICIENT ALIGNMENTS OF METABOLIC NETWORKS WITH BOUNDED TREEWIDTH 413<br /> <i>Qiong Cheng, Piotr Berman, Robert W. Harrison, and Alexander Zelikovsky</i></p> <p>22 PROTEIN–PROTEIN INTERACTION NETWORK ALIGNMENT: ALGORITHMS AND TOOLS 431<br /> <i>Valeria Fionda</i></p> <p><b>V APPLICATION OF PROTEIN BIOINFORMATICS</b></p> <p>23 PROTEIN-RELATED DRUG ACTIVITY COMPARISON USING SUPPORT VECTOR MACHINES 451<br /> <i>Wei Zhong and Jieyue He</i></p> <p>24 FINDING REPETITIONS IN BIOLOGICAL NETWORKS: CHALLENGES, TRENDS, AND APPLICATIONS 461<br /> <i>Simona E. Rombo</i></p> <p>25 MeTaDoR: ONLINE RESOURCE AND PREDICTION SERVER FOR MEMBRANE TARGETING PERIPHERAL PROTEINS 481<br /> <i>Nitin Bhardwaj, Morten Källberg, Wonhwa Cho, and Hui Lu</i></p> <p>26 BIOLOGICAL NETWORKS–BASED ANALYSIS OF GENE EXPRESSION SIGNATURES 495<br /> <i>Gang Chen and Jianxin Wang</i></p> <p>INDEX 507</p>

<p><b>YI PAN, P<small>H</small>D,</b> 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. <p><b>MIN LI, P<small>H</small>D,</b> 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. <p><b>JIANXIN WANG, P<small>H</small>D,</b> is Associate Dean and Full Professor in the School of Information Science and Engineering at Central South University in Changsha, China.

<p>An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics <p>This 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, <i>Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics</i> 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. <p>Featuring 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. <i>Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics:</i> <ul><li>Highlights protein analysis applications such as protein-related drug activity comparison</li> <li>Incorporates salient case studies illustrating how to apply the methods outlined in the book</li> <li>Tackles the complex relationship between proteins from a systems biology point of view</li> <li>Relates the topic to other emerging technologies such as data mining and visualization</li> <li>Includes many tables and illustrations demonstrating concepts and performance figures</li></ul> <p><i>Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics</i> is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.

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