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Structural Biology and Functional Genomics
Biomedical research will be revolutionised by the current efforts to sequence the human genome and the genomes of model organisms. Of the newly sequenced genes, 50% code for proteins of unknown functions, while as little as 5% of sequences in mammalian genomes code for proteins. New, genome-wide approaches are needed to draw together the knowledge that is emerging simultaneously in a number of fields of genome research. This volume is a high-level survey of the newly emerging concepts of structural biology and functional genomics for biologists, biochemists and medical researchers interested in genome research. Topics included are chromosome and chromatin organisation, novel DNA and RNA structures, DNA flexibility, supercoiling, prediction of protein functions, strategies for large scale structural analysis, and computer modelling.
Information Theory and Statistical Learning
"Information Theory and Statistical Learning" presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts. Each chapter is written by an expert in the field. The book is intended for an interdisciplinary readership working in machine learning, applied statistics, artificial intelligence, biostatistics, computational biology, bioinformatics, web mining or related disciplines. Advance Praise for "Information Theory and Statistical Learning": "A new epoch has arrived for information sciences to integrate various disciplines such as information theory, machine learning, statistical inference, data mining, model selection etc. I am enthusiastic about recommending the present book to researchers and students, because it summarizes most of these new emerging subjects and methods, which are otherwise scattered in many places." Shun-ichi Amari, RIKEN Brain Science Institute, Professor-Emeritus at the University of Tokyo
LuxR Solos Are Becoming Major Players in Cell-Cell Communication in Bacteria
The most common quorum sensing (QS) system in Gram-negative bacteria occurs via N-acyl homoserine lactone (AHLs) signals. An archetypical system consists of a LuxI-family protein synthesizing the AHL signal which binds at quorum concentrations to the cognate LuxR-family transcription factors which then control gene expression by binding to specific sequences in target gene promoters. QS LuxR-family proteins are approximately 250 amino acids long and made up of two domains; at the N-terminus there is an autoinducer-binding domain whereas the C-terminus contains a DNA-binding helix-turn-helix (HTH) domain. QS LuxRs display surprisingly low similarities (18-25%) even if they respond to structur...
Supramolecular Structure and Function 7
This volume represents a collection of lectures delivered by outstanding specialists in the fields of biophysics and of related scientific disciplines th during the 7 International Summer School on Biophysics held in Rovinj, Croatia from 14 to 25 September 2000 under the title "Super molecular Structure and Function ". This scientific-educational event was organized by the Ruder Boskovic Institute ofZagreb, Croatia with substantial material and intellectual support of a number of national and international institutions including the Croatian Biophysical Society (CBS), the International Union of Pure and Applied Biophysics (IUPAB), the International Centre for Genetic Engineering and Biotechn...
Theoretical Organic Chemistry
This volume is devoted to the various aspects of theoretical organic chemistry. In the nineteenth century, organic chemistry was primarily an experimental, empirical science. Throughout the twentieth century, the emphasis has been continually shifting to a more theoretical approach. Today, theoretical organic chemistry is a distinct area of research, with strong links to theoretical physical chemistry, quantum chemistry, computational chemistry, and physical organic chemistry.The objective in this volume has been to provide a cross-section of a number of interesting topics in theoretical organic chemistry, starting with a detailed account of the historical development of this discipline and including topics devoted to quantum chemistry, physical properties of organic compounds, their reactivity, their biological activity, and their excited-state properties.
Rethinking Cancer
Leading scientists argue for a new paradigm for cancer research, proposing a complex systems view of cancer supported by empirical evidence. Current consensus in cancer research explains cancer as a disease caused by specific mutations in certain genes. After dramatic advances in genome sequencing, never before have we known so much about the individual cancer cell--and yet never before has it been so unclear what to do with this knowledge. In this volume, leading researchers argue for a new theory framework for understanding and treating cancer. The contributors propose a complex systems view of cancer, presenting conceptual building blocks for a new research paradigm supported by empirical...
Machine Learning and Data Mining in Pattern Recognition
Ever wondered what the state of the art is in machine learning and data mining? Well, now you can find out. This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, held in Leipzig, Germany, in July 2007. The 66 revised full papers presented together with 1 invited talk were carefully reviewed and selected from more than 250 submissions. The papers are organized in topical sections.
Machine Learning and Knowledge Discovery in Databases, Part II
This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.
