Download Computational Learning Approaches to Data Analytics in Biomedical Applications Ebook PDF

Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications
A Book

by Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch

  • Publisher : Academic Press
  • Release : 2019-11-29
  • Pages : 310
  • ISBN : 0128144831
  • Language : En, Es, Fr & De
GET BOOK

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor

Deep Learning for Data Analytics

Deep Learning for Data Analytics
Foundations, Biomedical Applications, and Challenges

by Himansu Das,Chittaranjan Pradhan,Nilanjan Dey

  • Publisher : Academic Press
  • Release : 2020-05-29
  • Pages : 218
  • ISBN : 0128226080
  • Language : En, Es, Fr & De
GET BOOK

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
A Book

by K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh

  • Publisher : CRC Press
  • Release : 2020-10-07
  • Pages : 250
  • ISBN : 1000179516
  • Language : En, Es, Fr & De
GET BOOK

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning

Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications
Challenges and Solutions

by Hemanth D. Jude,Deepak Gupta,Valentina Emilia Balas

  • Publisher : Academic Press
  • Release : 2019-03-15
  • Pages : 294
  • ISBN : 0128156430
  • Language : En, Es, Fr & De
GET BOOK

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases. Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection Contains an analysis of medical databases to provide diagnostic expert systems Addresses the integration of intelligent data analysis techniques within biomedical information systems

Data Science and Predictive Analytics

Data Science and Predictive Analytics
Biomedical and Health Applications using R

by Ivo D. Dinov

  • Publisher : Springer
  • Release : 2018-04-07
  • Pages : 329
  • ISBN : 3319723472
  • Language : En, Es, Fr & De
GET BOOK

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. The two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). This can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook. • A geriatric neurologist is examining a patient complaining of gait imbalance and posture instability. To determine if the patient may suffer from Parkinson’s disease, the physician acquires clinical, cognitive, phenotypic, imaging, and genetics data (Big Data). Most clinics and healthcare centers are not equipped with skilled data analytic teams that can wrangle, harmonize and interpret such complex datasets. A learner that completes a course of study using this textbook will have the competency and ability to manage the data, generate a protocol for deriving biomarkers, and provide an actionable decision support system. The results of this protocol will help the physician understand the entire patient dataset and assist in making a holistic evidence-based, data-driven, clinical diagnosis. • To improve the return on investment for their shareholders, a healthcare manufacturer needs to forecast the demand for their product subject to environmental, demographic, economic, and bio-social sentiment data (Big Data). The organization’s data-analytics team is tasked with developing a protocol that identifies, aggregates, harmonizes, models and analyzes these heterogeneous data elements to generate a trend forecast. This system needs to provide an automated, adaptive, scalable, and reliable prediction of the optimal investment, e.g., R&D allocation, that maximizes the company’s bottom line. A reader that complete a course of study using this textbook will be able to ingest the observed structured and unstructured data, mathematically represent the data as a computable object, apply appropriate model-based and model-free prediction techniques. The results of these techniques may be used to forecast the expected relation between the company’s investment, product supply, general demand of healthcare (providers and patients), and estimate the return on initial investments.

Introduction to Biomedical Data Science

Introduction to Biomedical Data Science
A Book

by Robert Hoyt,Robert Muenchen

  • Publisher : Lulu.com
  • Release : 2019-11-25
  • Pages : 258
  • ISBN : 179476173X
  • Language : En, Es, Fr & De
GET BOOK

Introduction to Biomedical Data Science aims to fill the data science knowledge gap experienced by many clinical, administrative and technical staff. The textbook begins with an overview of what biomedical data science is and then embarks on a tour of topics beginning with spreadsheet tips and tricks and ending with artificial intelligence. In between, important topics are covered such as biostatistics, data visualization, database systems, big data, programming languages, bioinformatics, and machine learning. The textbook is available as a paperback and ebook. Visit the companion website at https: //www.informaticseducation.org for more information. Key features: Real healthcare datasets are used for examples and exercises; Knowledge of a programming language or higher math is not required; Multiple free or open source software programs are presented; YouTube videos are embedded in most chapters; Extensive resources chapter for further reading and learning; PowerPoints and an Instructor Manual

Machine Learning for Biomedical Applications

Machine Learning for Biomedical Applications
A Book

by Maria Deprez,Emma C. Robinson

  • Publisher : Academic Press
  • Release : 2022-04-15
  • Pages : 325
  • ISBN : 9780128229040
  • Language : En, Es, Fr & De
GET BOOK

Machine Learning for Biomedical Applications presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning, where concepts are presented in short descriptions followed by solving simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. The book is divided into four Parts: A general background to machine learning techniques and their use in biomedical applications, practical Python coding skills, and mathematical tool that underpin the field; core machine learning methods; Deep learning concepts with examples in Keras. ; tricks of the trade where guidance is given on best practice for data preparation and experimental design to aid the successful application of machine learning methods to real world biomedical data. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, biomedical science, and clinicians. Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis Shows to apply a range of commonly used machine learning and deep learning techniques to biomedical problems Develops practical computational skills that are needed to manipulate complex biomedical data sets Shows how to design machine learning experiments that address specific problems related to biomedical data

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
A Book

by Rani, Geeta,Tiwari, Pradeep Kumar

  • Publisher : IGI Global
  • Release : 2020-10-16
  • Pages : 586
  • ISBN : 1799827437
  • Language : En, Es, Fr & De
GET BOOK

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Applied Machine Learning for Smart Data Analysis

Applied Machine Learning for Smart Data Analysis
A Book

by Nilanjan Dey,Sanjeev Wagh,Parikshit N. Mahalle,Mohd. Shafi Pathan

  • Publisher : CRC Press
  • Release : 2019-05-20
  • Pages : 225
  • ISBN : 0429804563
  • Language : En, Es, Fr & De
GET BOOK

The book focusses on how machine learning and Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results.

Computational Methods With Applications In Bioinformatics Analysis

Computational Methods With Applications In Bioinformatics Analysis
A Book

by Tsai Jeffrey J P,Ng Ka-lok

  • Publisher : World Scientific
  • Release : 2017-06-09
  • Pages : 232
  • ISBN : 981320799X
  • Language : En, Es, Fr & De
GET BOOK

This compendium contains 10 chapters written by world renowned researchers with expertise in semantic computing, genome sequence analysis, biomolecular interaction, time-series microarray analysis, and machine learning algorithms. The salient feature of this book is that it highlights eight types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations and enzyme kinetics. The unique volume will be a useful reference material and an inspirational read for advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.

Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques

Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Algorithms, Methods, and Techniques

by Olivas, Emilio Soria,Guerrero, Jos‚ David Mart¡n,Martinez-Sober, Marcelino,Magdalena-Benedito, Jose Rafael,Serrano L¢pez, Antonio Jos‚

  • Publisher : IGI Global
  • Release : 2009-08-31
  • Pages : 852
  • ISBN : 1605667676
  • Language : En, Es, Fr & De
GET BOOK

"This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
12th Conference on Artificial Intelligence in Medicine in Europe, AIME 2009, Verona, Italy, July 18-22, 2009, Proceedings

by Carlo Combi,Yuval Shahar,Ameen Abu-Hanna

  • Publisher : Springer Science & Business Media
  • Release : 2009-07-13
  • Pages : 439
  • ISBN : 3642029760
  • Language : En, Es, Fr & De
GET BOOK

This book constitutes the refereed proceedings of the 12th Conference on Artificial Intelligence in Medicine in Europe, AIME 2009, held in Verona, Italy in July 2009. The 24 revised long papers and 36 revised short papers presented together with 2 invited talks were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on agent-based systems, temporal data mining, machine learning and knowledge discovery, text mining, natural language processing and generation, ontologies, decision support systems, applications of AI-based image processing techniques, protocols and guidelines, as well as workflow systems.

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
A Book

by Guorong Wu,Dinggang Shen,Mert Sabuncu

  • Publisher : Academic Press
  • Release : 2016-08-11
  • Pages : 512
  • ISBN : 0128041145
  • Language : En, Es, Fr & De
GET BOOK

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Machine Learning: Concepts, Methodologies, Tools and Applications

Machine Learning: Concepts, Methodologies, Tools and Applications
Concepts, Methodologies, Tools and Applications

by Management Association, Information Resources

  • Publisher : IGI Global
  • Release : 2011-07-31
  • Pages : 2124
  • ISBN : 1609608194
  • Language : En, Es, Fr & De
GET BOOK

"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe

Big Data Analytics in Bioinformatics and Healthcare

Big Data Analytics in Bioinformatics and Healthcare
A Book

by Wang, Baoying

  • Publisher : IGI Global
  • Release : 2014-10-31
  • Pages : 528
  • ISBN : 1466666129
  • Language : En, Es, Fr & De
GET BOOK

As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.

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

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

by Pradeep Nijalingappa,Sandeep Kautish,Sheng Lung Peng

  • Publisher : Academic Press
  • Release : 2021-06-01
  • Pages : 332
  • ISBN : 0128220449
  • Language : En, Es, Fr & De
GET BOOK

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents how these emerging areas are changing the world of data utilization, especially in clinical healthcare. Various techniques, methodologies and algorithms are presented in a structured manner to assist physicians in the precision care of patients and help biomedical engineers and computers scientists understand the impact of these techniques on healthcare analytics. Sections cover Big Data aspects, i.e., healthcare Decision Support Systems and Analytics related topics, focus on current frameworks and applications of Deep Learning and Machine Learning, and provide an outlook on future directions. The entire book takes a case study approach, providing a wealth of real-world case studies that act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables and graphs with algorithms and computational methods for developing new applications in healthcare informatics Presents a unique case study approach that provides readers with insights for practical clinical implementations

Real-Time Data Analytics for Large Scale Sensor Data

Real-Time Data Analytics for Large Scale Sensor Data
A Book

by Himansu Das,Nilanjan Dey,Valentina Emilia Balas

  • Publisher : Academic Press
  • Release : 2019-08-31
  • Pages : 298
  • ISBN : 0128182423
  • Language : En, Es, Fr & De
GET BOOK

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more. Examines IoT applications, the design of real-time intelligent systems, and how to manage the rapid growth of the large volume of sensor data Discusses intelligent management systems for applications such as healthcare, robotics and environment modeling Provides a focused approach towards the design and implementation of real-time intelligent systems for the management of sensor data in large-scale environments

Data Analysis, Machine Learning and Applications

Data Analysis, Machine Learning and Applications
Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7-9, 2007

by Christine Preisach,Hans Burkhardt,Lars Schmidt-Thieme,Reinhold Decker

  • Publisher : Springer Science & Business Media
  • Release : 2008-04-13
  • Pages : 719
  • ISBN : 9783540782469
  • Language : En, Es, Fr & De
GET BOOK

Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
A Book

by Ervin Sejdic,Tiago H. Falk

  • Publisher : CRC Press
  • Release : 2018-07-04
  • Pages : 606
  • ISBN : 1351061216
  • Language : En, Es, Fr & De
GET BOOK

This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals. It will give signal processing researchers a glimpse into the issues faced with Big Medical Data.

Applying Big Data Analytics in Bioinformatics and Medicine

Applying Big Data Analytics in Bioinformatics and Medicine
A Book

by Lytras, Miltiadis D.,Papadopoulou, Paraskevi

  • Publisher : IGI Global
  • Release : 2017-06-16
  • Pages : 465
  • ISBN : 1522526080
  • Language : En, Es, Fr & De
GET BOOK

Many aspects of modern life have become personalized, yet healthcare practices have been lagging behind in this trend. It is now becoming more common to use big data analysis to improve current healthcare and medicinal systems, and offer better health services to all citizens. Applying Big Data Analytics in Bioinformatics and Medicine is a comprehensive reference source that overviews the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Featuring coverage on relevant topics that include smart data, proteomics, medical data storage, and drug design, this publication is an ideal resource for medical professionals, healthcare practitioners, academicians, and researchers interested in the latest trends and techniques in personalized medicine.