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Probabilistic Modeling in Bioinformatics and Medical Informatics

2006-05-06 Computers
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Release : 2006-05-06
Genre : Computers
Kind : eBook
Book Rating : 199/5 ( reviews)

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Book Synopsis Probabilistic Modeling in Bioinformatics and Medical Informatics by : Dirk Husmeier

Download or read book Probabilistic Modeling in Bioinformatics and Medical Informatics written by Dirk Husmeier. This book was released on 2006-05-06. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Handbook of Statistical Bioinformatics

2022-12-08 Science
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Release : 2022-12-08
Genre : Science
Kind : eBook
Book Rating : 026/5 ( reviews)

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Book Synopsis Handbook of Statistical Bioinformatics by : Henry Horng-Shing Lu

Download or read book Handbook of Statistical Bioinformatics written by Henry Horng-Shing Lu. This book was released on 2022-12-08. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

Biological Sequence Analysis

1998-04-23 Science
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Release : 1998-04-23
Genre : Science
Kind : eBook
Book Rating : 39X/5 ( reviews)

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Book Synopsis Biological Sequence Analysis by : Richard Durbin

Download or read book Biological Sequence Analysis written by Richard Durbin. This book was released on 1998-04-23. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.

Machine Learning, Big Data, and IoT for Medical Informatics

2021-06-13 Computers
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Release : 2021-06-13
Genre : Computers
Kind : eBook
Book Rating : 812/5 ( reviews)

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Book Synopsis Machine Learning, Big Data, and IoT for Medical Informatics by : Pardeep Kumar

Download or read book Machine Learning, Big Data, and IoT for Medical Informatics written by Pardeep Kumar. This book was released on 2021-06-13. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. - Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. - Includes several privacy preservation techniques for medical data. - Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. - Offers case studies and applications relating to machine learning, big data, and health care analysis.

Artificial Intelligence in Medicine

2019-06-19 Computers
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Release : 2019-06-19
Genre : Computers
Kind : eBook
Book Rating : 42X/5 ( reviews)

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Book Synopsis Artificial Intelligence in Medicine by : David Riaño

Download or read book Artificial Intelligence in Medicine written by David Riaño. This book was released on 2019-06-19. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

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