Discriminative Pattern Discovery On Biological Networks

Author : Fabio Fassetti
Publisher : Springer
ISBN 13 : 3319634771
Total Pages : 45 pages
Book Rating : 4.77/5 ( download)

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Download or read book Discriminative Pattern Discovery on Biological Networks written by Fabio Fassetti and published by Springer. This book was released on 2017-09-01 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes). Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples). In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.

Author : Laxmi Parida
Publisher : CRC Press
ISBN 13 : 1420010735
Total Pages : 512 pages
Book Rating : 4.32/5 ( download)

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Download or read book Pattern Discovery in Bioinformatics written by Laxmi Parida and published by CRC Press. This book was released on 2007-07-04 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systema

Author : Zheng Rong Yang
Publisher : World Scientific
ISBN 13 : 9811240132
Total Pages : 462 pages
Book Rating : 4.33/5 ( download)

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Download or read book Biological Pattern Discovery With R: Machine Learning Approaches written by Zheng Rong Yang and published by World Scientific. This book was released on 2021-09-17 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.

Author : Guozhu Dong
Publisher : Springer Nature
ISBN 13 : 303101913X
Total Pages : 135 pages
Book Rating : 4.35/5 ( download)

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Download or read book Exploiting the Power of Group Differences written by Guozhu Dong and published by Springer Nature. This book was released on 2022-05-31 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.

Author : Li, Xiao-Li
Publisher : IGI Global
ISBN 13 : 1466617861
Total Pages : 464 pages
Book Rating : 4.65/5 ( download)

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Download or read book Computational Knowledge Discovery for Bioinformatics Research written by Li, Xiao-Li and published by IGI Global. This book was released on 2012-06-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book discusses the most significant research and latest practices in computational knowledge discovery approaches to bioinformatics in a cross-disciplinary manner that is useful for researchers, practitioners, academicians, mathematicians, statisticians, and computer scientists involved in the many facets of bioinformatics"--

Author : Yang Rong Zheng
Publisher :
ISBN 13 : 9789811240126
Total Pages : 462 pages
Book Rating : 4.24/5 ( download)

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Download or read book Biological Pattern Discovery with R written by Yang Rong Zheng and published by . This book was released on 2021 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Zhi-Hua Zhou
Publisher : Springer
ISBN 13 : 3540717013
Total Pages : 1161 pages
Book Rating : 4.10/5 ( download)

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Download or read book Advances in Knowledge Discovery and Data Mining written by Zhi-Hua Zhou and published by Springer. This book was released on 2007-06-21 with total page 1161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China, May 2007. It covers new ideas, original research results and practical development experiences from all KDD-related areas including data mining, machine learning, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition and knowledge-based systems.

Author : Jake Y. Chen
Publisher : CRC Press
ISBN 13 : 1420086855
Total Pages : 736 pages
Book Rating : 4.50/5 ( download)

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Download or read book Biological Data Mining written by Jake Y. Chen and published by CRC Press. This book was released on 2009-09-01 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplin

Author : Michael Gertz
Publisher : Springer Science & Business Media
ISBN 13 : 3642138179
Total Pages : 673 pages
Book Rating : 4.71/5 ( download)

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Download or read book Scientific and Statistical Database Management written by Michael Gertz and published by Springer Science & Business Media. This book was released on 2010-06-17 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 22nd International Conference on Scientific and Statistical Database Management, SSDBM 2010, held in Heidelberg, Germany in June/July 2010. The 30 long and 11 short papers presented were carefully reviewed and selected from 94 submissions. The topics covered are query processing; scientific data management and analysis; data mining; indexes and data representation; scientific workflow and provenance; and data stream processing.

Author : Derong Liu
Publisher : Springer
ISBN 13 : 3319701363
Total Pages : 912 pages
Book Rating : 4.63/5 ( download)

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Download or read book Neural Information Processing written by Derong Liu and published by Springer. This book was released on 2017-11-07 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.