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Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis;

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Statistical Modeling in Biomedical Research

This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers;

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Statistical Modeling in Biomedical Research: Contemporary Topics and Voices in the Field

This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers;

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Quantitative Medical Data Analysis Using Mathematical Tools And Statistical Techniques

biomedical sciences. Topics include mathematical models for cancer invasion and clinical sciences, data mining techniques and subset selection;

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Statistical Learning for Biomedical Data

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as;

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Computational Methods in Biomedical Research

Continuing advances in biomedical research and statistical methods call for a constant stream of updated, cohesive accounts of new;

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Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences A practical guide to the use of basic principles;

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Computational Methods in Biomedical Research

Continuing advances in biomedical research and statistical methods call for a constant stream of updated, cohesive accounts of new;

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Statistics for Biomedical Engineers and Scientists

Statistics for Biomedical Engineers and Scientists: How to Analyze and Visualize Data provides an intuitive understanding of the;

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Signal Processing and Machine Learning for Biomedical Big Data

alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the;

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Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition;

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Signal Processing in Medicine and Biology

statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson's; machine learning methods and their;

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Data Analytics in Biomedical Engineering and Healthcare

medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data;

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Research in the Biomedical Sciences

standardization (including validation and authentication), statistical analysis, and data reporting. The book addresses issues in the perceived value;

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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization;

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R For Medicine And Biology

, processing, sharing, and analyzing biomedical data. R for Medicine and Biology is a step-by-step guide through the use of the statistical;

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine;

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Deep Learning for Data Analytics

of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications;

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Handbook of Deep Learning in Biomedical Engineering

composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the;

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Introduction to Statistics for Biomedical Engineers

perform the numerical summary and statistical analysis used in the biomedical field. This text is considered a starting point for important issues;

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Introduction to Statistics for Biomedical Engineers

perform the numerical summary and statistical analysis used in the biomedical field. This text is considered a starting point for important issues;

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Data Mining for Biomarker Discovery

methods, and statistical machine learning techniques. This volume is intended for students, and researchers in bioinformatics, proteomics, and;

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Data Science and Predictive Analytics

, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Medical Product Safety Evaluation

self-learning materials or in short courses or training programs, but also for graduate students in statistics and biomedical data science for;

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Medical Product Safety Evaluation

self-learning materials or in short courses or training programs, but also for graduate students in statistics and biomedical data science for;

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Statistical Advances in the Biomedical Sciences

The Most Comprehensive and Cutting-Edge Guide to Statistical Applications in Biomedical Research With the increasing use of biotechnology;

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