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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009, held in Tübingen, Germany, in April 2009 colocated with the Evo* 2009 events. The 17 revised full papers were carefully reviewed and selected from 44 submissions. EvoBio is the premiere European event for experts in computer science meeting with experts in bioinformatics and the biological sciences, all interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology. Topics addressed by the papers include biomarker discovery, cell simulation and modeling, ecological modeling, uxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, as well as systems biology.
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses.
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction,leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci'c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o'er the ?eld of bioinformatics. The goal of the 8th - ropean ...
Problems of Han Administration
Michael Loewe calls on literary and material evidence to examine three problems that arose in administering China’s early empires. Religious rites due to an emperor’s predecessors must both pay the correct services to his ancestors and demonstrate his right to succeed to the throne. In practical terms, tax collectors, merchants, farmers and townsmen required the establishment of a standard set of weights and measures that was universally operative and which they could trust. Those who saw reason to criticise the decisions taken by the emperor and his immediate advisors, whether on grounds of moral principles or political expediency, needed opportunities and the means of expressing their views, whether as remonstrants to the throne, by withdrawal from public life or as authors of private writings.
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Coverage in this proceedings volume includes biomarker discovery, cell simulation and modeling, ecological modeling, gene networks, biotechnology, microarray analysis, protein interactions, proteomics, sequence analysis and alignment, and systems biology
Principles of Statistical Genomics
Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle on the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach. Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data. Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.
Pattern Recognition in Bioinformatics
This book constitutes the refereed proceedings of the 5th International Conference on Pattern Recognition in Bioinformatics, PRIB 2010, held in Nijmegen, The Netherlands, in September 2010. The 38 revised full papers presented were carefully reviewed and selected from 46 submissions. The field of bioinformatics has two main objectives: the creation and maintenance of biological databases and the analysis of life sciences data in order to unravel the mysteries of biological function. Computer science methods such as pattern recognition, machine learning, and data mining have a great deal to offer the field of bioinformatics.
Precision Molecular Pathology of Prostate Cancer
This volume focuses on our current understanding of the molecular underpinnings of prostate cancer and their potential application for precision medicine approaches. The emergence and applications of new technologies has allowed for a rapid expansion of our understanding of the molecular basis of prostate cancer and has revealed a remarkable genetic heterogeneity that may underlie the clinically variable behavior of the disease. The book consists of five sections which provide insight about the following: (1) General principles; (2) Molecular signatures of primary prostate cancer; (3) Molecular signatures of advanced prostate cancer; (4) Key molecular pathways in prostate cancer development and progression; (5) and Precision medicine approach: Diagnosis, treatment, prognosis. Precision Molecular Pathology of Prostate Cancer is an important resource for the practicing oncologist, urologist, and pathologist, and will also be useful for researchers in the prostate cancer community.
