Neural networks and genome informatics - IEEE Xplore

mentoring programs and tells how participants related to the programs, to each other, and to the program’s lasting effects on both their personal and professional lives. The book gives definitions and narratives for 12 opposing actions, termed polarities, that push and pull us in different directions. The polarities are: ➤ welcoming and excluding ➤ communicating and bickering ➤ trusting and doubting ➤ accepting and rejecting ➤ affirming and ridiculing ➤ forgiving and condemning ➤ reframing and stagnating ➤ letting go and holding tight ➤ rejoicing and grieving ➤ balancing and tilting ➤ focusing and blurring ➤ gracing and alienating. In the book chapters, Dr. Wadsworth relates how she “caught” and “cast” actions that former staff members “caught” and then “recast” in their own personal and professional lives. Through the book, we learn the importance of: ➤ reaching out and welcoming others ➤ listening and communicating with others ➤ being a reliable person ➤ accepting and appreciating our differences and diversity ➤ encouraging commitment and ownership ➤ learning from mistakes ➤ reframing situations from life ➤ being yourself ➤ catching joy ➤ balancing work with leisure ➤ focusing, reflecting, and regrouping ourselves ➤ giving and receiving grace. I strongly recommend this book to men and women in engineering and nonengineering disciplines who value the importance of human relationships. The book is very well written and edited. I had the impression that the author and contributors were talking with me (not to me). It is an excellent book that can be used for self-improvement by any gender, age, and ethnicity group. —Semahat Demir 94

Neural Networks and Genome Informatics (Methods in Computational Biology and Biochemistry, Volume 1)

By Cathy H. Wu and Jerry W. McLarty. Elsevier Science, 2000. ISBN: 0-08042800-2220. Hardbound, US$91.00. This book aims to assist in bridging the gap between molecular biology and artificial neural network technology. It was written to address the important issues in applying neural network technology in genome informatics. Its intended audience includes molecular biologists and researchers interested in artificial neural networks, and it is also meant to be of value to biomedical engineers working in genome informatics applications. The text consists of 13 chapters, which can be divided into four parts, visibly. Part I comprises one chapter (Chapter 1) to provide an overview of genome informatics and artificial neural networks, respectively, and a brief introduction to genome informatics applications. Part II contains four chapters, each of which begins with a brief introduction to the subject at hand. This part of the book covers the foundation of basic neural network principles and focuses on the data encoding preparation for use by neural networks and how to choose the neural network architecture for particular applications. Chapter 2 briefly presents the commonly used neural network elements. The next two chapters review some of the best-known network architectures implemented in genome informatics, such as the multilayer perceptrons (Chapter 3), radial basis function networks, and Kohonen’s self organizing maps (Chapter 4). Chapter 5 discusses two fundamental kinds of learning performed by neural networks; i.e., supervised learning and unsupervised learning. Part III places emphasis on special system designs and considerations for building neural networks for genome informatics applications and broad reviews of state-of-the-art methods and their evaluations. This part, which involves six chapters, has two main subdivisions. The first section provides an in-depth discussion of specific designs issues, such as important amino acid and protein features and methods for feature representation

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(Chapter 6), experimental methods for data encoding (Chapter 7), and some aspects of neural networks design (Chapter 8). The second section describes several genome informatics applications. Chapter 9 expatiates on hybrid neural networks used in DNA coding region recognition and gene identification, transcription and translational signal recognition, feature analysis and extraction, and nucleic acid sequence classification. Chapter 10 covers the neural network systems in protein structure prediction, including protein secondary/tertiary structure prediction, structural classification, and structural feature analyses. Chapter 11 reviews neural networks and related systems in protein sequence analysis, especially the predictions of signal sequences and other motif regions or sites, and protein family identification as well. Part IV concludes with a discussion of integration of statistical methods into neural networks (Chapter 12) and a prediction of the future of genome informatics applications (Chapter 13). This book appears as Volume I of Methods in Computational Biology and Biochemistry Series (edited by A.K. Konopka), and it is currently the only book in the series that has been published. It gives a good example of introducing artificial neural network technology to the research of genome informatics, and it is valuable for both molecular biologists and bioinformatics researchers. However, genome informatics is one of the interdisciplinary areas that are moving rapidly in recent years. Current genome projects, especially the Human Genome Project, have sparked interest in the information encoded in DNA. To fully realize the value of the vast amounts of DNA sequence data and gain a full understanding of the genome, computational tools and techniques are needed to identify the biologically relevant features in the sequences and to provide insight into their structure and function (see Part I, Overview). I am looking forward to reading the next book of Methods in Computational Biology and Biochemistry Series in the near future. —Yunfeng Wu Beijing University of Posts and Telecommunications JANUARY/FEBRUARY 2003