Statistical Modeling And Machine Learning For Molecular Biology
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Statistical Modeling And Machine Learning For Molecular Biology
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Author : Alan Moses
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2017
Statistical Modeling And Machine Learning For Molecular Biology written by Alan Moses and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Molecular biology categories.
The book covers several of the major data analysis techniques used to analyze data from high-throughput molecular biology and genomics experiments. It also explains the major concepts behind most of the popular techniques and examines some of the simpler techniques in detail.
Statistical Modeling And Machine Learning For Molecular Biology
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Author : Alan Moses
language : en
Publisher: CRC Press
Release Date : 2017-01-06
Statistical Modeling And Machine Learning For Molecular Biology written by Alan Moses and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-06 with Computers categories.
• Assumes no background in statistics or computers • Covers most major types of molecular biological data • Covers the statistical and machine learning concepts of most practical utility (P-values, clustering, regression, regularization and classification) • Intended for graduate students beginning careers in molecular biology, systems biology, bioengineering and genetics
Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications
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Author : K. G. Srinivasa
language : en
Publisher: Springer Nature
Release Date : 2020-01-30
Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications written by K. G. Srinivasa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-30 with Technology & Engineering categories.
This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
Gene Expression Data Analysis
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Author : Pankaj Barah
language : en
Publisher: CRC Press
Release Date : 2021-11-08
Gene Expression Data Analysis written by Pankaj Barah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-08 with Computers categories.
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences
Probabilistic Modeling In Bioinformatics And Medical Informatics
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Author : Dirk Husmeier
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-06
Probabilistic Modeling In Bioinformatics And Medical Informatics written by Dirk Husmeier and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-06 with Computers categories.
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
Machine Learning In Biomolecular Simulations
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Author : Gennady Verkhivker
language : en
Publisher: Frontiers Media SA
Release Date : 2019-10-21
Machine Learning In Biomolecular Simulations written by Gennady Verkhivker and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-21 with categories.
Machine learning methods such as neural networks, non-linear dimensionality reduction techniques, random forests and others meet in this research topic with biomolecular simulations. The authors of eight articles applied these methods to analyze simulation results, accelerate simulations or to make molecular mechanics force fields more accurate.
Statistical Modelling Of Molecular Descriptors In Qsar Qspr
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Author : Matthias Dehmer
language : en
Publisher: John Wiley & Sons
Release Date : 2012-03-26
Statistical Modelling Of Molecular Descriptors In Qsar Qspr written by Matthias Dehmer and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-26 with Medical categories.
This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. The high-profile international author and editor team ensures excellent coverage of the topic, making this a must-have for everyone working in chemoinformatics and structure-oriented drug design.
Bioinformatics Second Edition
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Author : Pierre Baldi
language : en
Publisher: MIT Press
Release Date : 2001-07-20
Bioinformatics Second Edition written by Pierre Baldi and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-07-20 with Computers categories.
A guide to machine learning approaches and their application to the analysis of biological data. An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models—and to automate the process as much as possible. In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology. This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.
Deep Learning Machine Learning And Iot In Biomedical And Health Informatics
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Author : Sujata Dash
language : en
Publisher: CRC Press
Release Date : 2022-02-10
Deep Learning Machine Learning And Iot In Biomedical And Health Informatics written by Sujata Dash and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-10 with Computers categories.
Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems
Gene Expression Data Analysis
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Author : Pankaj Barah
language : en
Publisher: CRC Press
Release Date : 2021-11-21
Gene Expression Data Analysis written by Pankaj Barah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-21 with Computers categories.
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences