Applying Analytics Bioinformatics Biomedical Engineering 3 pdf pdf

Applying Big Data Analytics in

Bioinformatics and

Medicine Miltiadis D. Lytras Deree - The American College of Greece, Greece Paraskevi Papadopoulou Deree - The American College of Greece, Greece A volume in the Advances in Bioinformatics and Biomedical Engineering (ABBE) Book Series

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Library of Congress Cataloging-in-Publication Data Names: Lytras, Miltiadis D., 1973- editor. | Papadopoulou, Paraskevi, 1961- editor. Title: Applying big data analytics in bioinformatics and medicine / Miltiadis D. Lytras and Paraskevi Papadopoulou, editors. Description: Hershey PA : Medical Information Science Reference, [2018] | Includes bibliographical references and index. Identifiers: LCCN 2017006764| ISBN 9781522526070 (hardcover) | ISBN 9781522526087 (ebook) Subjects: | MESH: Databases, Factual | Computational Biology | Medical Records Systems, Computerized | Organizational Culture Classification: LCC QH324.2 | NLM W 26.55.I4 | DDC 570.285--dc23 LC record available at https://lccn.loc. gov/2017006764 This book is published in the IGI Global book series Advances in Bioinformatics and Biomedical Engineering (ABBE) (ISSN: 2327-7033; eISSN: 2327-7041) British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library.

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Comparative Approaches to Biotechnology Development and Use in Developed and Emerging Nations Tomas Gabriel Bas (University of Talca, Chile) and Jingyuan Zhao (University of Toronto, Canada)

(our price) Computational Tools and Techniques for Biomedical Signal Processing Butta Singh (Guru Nanak Dev University, India)

(our price) Handbook of Research on Computational Intelligence Applications in Bioinformatics Sujata Dash (North Orissa University, India) and Bidyadhar Subudhi (National Institute of Technology, India)

(our price) Applying Business Intelligence to Clinical and Healthcare Organizations José Machado (University of Minho, Portugal) and António Abelha (University of Minho, Portugal)

(our price) Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes

Wahiba Ben Abdessalem Karâa (Taif University, Saudi Arabia & RIADI-GDL Laboratory, ENSI, Tunisia) and

Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)

(our price) Big Data Analytics in Bioinformatics and Healthcare

Baoying Wang (Waynesburg University, USA) Ruowang Li (Pennsylvania State University, USA) and William

Perrizo (North Dakota State University, USA)

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Table of Contents

Preface ................................................................................................................................................. xvi

Acknowledgment .............................................................................................................................. xxvi

Section 1

Introduction to Bioinformatics in Medicine and Medical Systems

Chapter 1 Bioinformatics as Applied to Medicine: Challenges Faced Moving from Big Data to Smart Data

to Wise Data ............................................................................................................................................ 1

Paraskevi Papadopoulou, Deree – The American College of Greece, Greece Miltiadis Lytras, Deree – The American College of Greece, Greece Christina Marouli, Deree – The American College of Greece, Greece

Chapter 2 Bioinformatics: Applications and Implications .................................................................................... 26 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand Chapter 3 Protein Structure Prediction .................................................................................................................. 48 Hirak Jyoti Chakraborty, Central Inland Fisheries Research Institute, India Aditi Gangopadhyay, Jhargram Raj College, India Sayak Ganguli, Amplicon Institute of Interdisciplinary Science and Technology, India Abhijit Datta, Jhargram Raj College, India Chapter 4 Proteomics in Personalized Medicine: An Evolution Not a Revolution ............................................... 80 Srijan Goswami, Institute of Genetic Engineering, Badu, India Chapter 5 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage .......... 99 Sushma Munugala, Charles Sturt University, Australia Gagandeep K. Brar, Charles Sturt University, Australia Ali Syed, Charles Sturt University, Australia Azeem Mohammad, Charles Sturt University, Australia Malka N. Halgamuge, Charles Sturt University, Australia

Section 2

Bioinformatics in the Fields of Genomics and Proteomics as Applied to Medicine, Health

Issues, and Medical Systems

Chapter 6 Informatics and Data Analytics to Support Exposome-Based Discovery: Part 1 - Assessment of External and Internal Exposure .......................................................................................................... 115 Dimosthenis A. Sarigiannis, Aristotle University of Thessaloniki, Greece Spyros P. Karakitsios, Aristotle University of Thessaloniki, Greece Evangelos Handakas, Aristotle University of Thessaloniki, Greece Krystalia Papadaki, Aristotle University of Thessaloniki, Greece Dimitris Chapizanis, Aristotle University of Thessaloniki, Greece Alberto Gotti, Aristotle University of Thessaloniki, Greece Chapter 7 Informatics and Data Analytics to Support Exposome-Based Discovery: Part 2 - Computational Exposure Biology................................................................................................................................ 145 Dimosthenis A. Sarigiannis, Aristotle University of Thessaloniki, Greece Alberto Gotti, Aristotle University of Thessaloniki, Greece Evangelos Handakas, Aristotle University of Thessaloniki, Greece Spyros P. Karakitsios, Aristotle University of Thessaloniki, Greece Chapter 8 Transcriptomics to Metabolomics: A Network Perspective for Big Data ........................................... 188 Ankush Bansal, Jaypee University of Information Technology, India Pulkit Anupam Srivastava, Jaypee University of Information Technology, India Chapter 9 Protein Docking and Drug Design ...................................................................................................... 207 Aditi Gangopadhyay, Jhargram Raj College, India Hirak Jyoti Chakraborty, Central Inland Fisheries Research Institute, India Abhijit Datta, Department of Botany, Jhargram Raj College, India

Section 3

Big Data Analytics for Medical and Health informatics

Chapter 10 Effective and Efficient Business Intelligence Dashboard Design: Gestalt Theory in Dutch Long- Term and Chronic Healthcare ............................................................................................................. 243 Marco Spruit, Utrecht University, The Netherlands Max Lammertink, Utrecht University, The Netherlands Chapter 11 Role of Online Data from Search Engine and Social Media in Healthcare Informatics..................... 272 M. Saqib Nawaz, Peking University, China Raza Ul Mustafa, COMSATS Institute of IT, Sahiwal, Pakistan M. Ikram Ullah Lali, University of Sargodha, Pakistan

Chapter 12 An Optimized Semi-Supervised Learning Approach for High Dimensional Datasets ....................... 294 Nesma Settouti, Tlemcen University, Algeria Mostafa El Habib Daho, Tlemcen University, Algeria Mohammed El Amine Bechar, Tlemcen University, Algeria Mohammed Amine Chikh, Tlemcen University, Algeria Chapter 13 Predicting Patterns in Hospital Admission Data ................................................................................. 322 Jesús Manuel Puentes Gutiérrez, Universidad de Alcalá, Spain Salvador Sánchez-Alonso, Universidad de Alcalá, Spain Miguel-Angel Sicilia, University of Alcalá, Spain Elena García Barriocanal, Universidad de Alcalá, Spain Chapter 14 Selection of Pathway Markers for Cancer Using Collaborative Binary Multi-Swarm Optimization ....................................................................................................................................... 337 Prativa Agarwalla, Heritage Institute of Technology, India Sumitra Mukhopadhyay, Institute of Radiophysics and Electronics, India Chapter 15 Applying Bayesian Networks in the Early Diagnosis of Bulimia and Anorexia Nervosa in Adolescents: Applying Bayesian Networks in Early Diagnosis in Adolescents ................................. 364 Placido Rogerio Pinheiro, University of Fortaleza, Brazil Mirian Caliope Dantas Pinheiro, University of Fortaleza, Brazil Victor Câmera Damasceno, University of Fortaleza, Brazil Marley Costa Marques, University of Fortaleza, Brazil Raquel Souza Bino Araújo, University of Fortaleza, Brazil Layane Mayara Gomes Castelo Branco, University of Fortaleza, Brazil Chapter 16 Image Processing Including Medical Liver Imaging: Medical Image Processing from Big Data Perspective, Ultrasound Liver Images, Challenges ............................................................................. 380 Suganya Ramamoorthy, Thiagarajar College of Engineering, India Rajaram Sivasubramaniam, Thiagarajar College of Engineering, India

Compilation of References ............................................................................................................... 393

About the Contributors .................................................................................................................... 454

Index ................................................................................................................................................... 463

Detailed Table of Contents

Preface ................................................................................................................................................. xvi

Acknowledgment .............................................................................................................................. xxvi

Section 1

Introduction to Bioinformatics in Medicine and Medical Systems

This section covers topics related to inferring gene function from expression data, genome sequence data,

integrating expression data with other genome-wide data for functional annotation, functional study of

specific molecular and pathway analysis, genome annotation and comparative genomics bioinformatics

algorithm and tool development, RNAseq and microarray gene expression data analysis, gene regulatory

network construction and Next-Generation Sequencing (NGS) analysis. Furthermore, this section covers

topics related to translational bioinformatics, protein sequencing and classification, protein structure

prediction, protein function analysis, protein interactions, protein subcellular localization prediction.

Chapter 1 Bioinformatics as Applied to Medicine: Challenges Faced Moving from Big Data to Smart Data

to Wise Data ............................................................................................................................................ 1

The emerging advances of Bioinformatics have already contributed toward the establishment of better next generation medicine and medical systems by putting emphasis on improvement of prognosis, diagnosis and therapy of diseases including better management of medical systems. The purpose of this chapter is to explore ways by which the use of Bioinformatics and Smart Data Analysis will provide an overview and solutions to challenges in the fields of genomics, medicine and Health Informatics. The focus of this chapter would be on Smart Data Analysis and ways needed to filter out the noise. The chapter addresses challenges researchers and data analysts are facing in terms of the developed computational methods used to extract insights from NGS and high-throughput screening data. In this chapter the concept “Wise Data” is proposed reflecting the distinction between individual health and wellness on the one hand, and social improvement, cohesion and sustainability on the other, leading to more effective medical systems, healthier individuals and more socially cohesive societies.

Chapter 2 Bioinformatics: Applications and Implications .................................................................................... 26 Kijpokin Kasemsap, Suan Sunandha Rajabhat University, Thailand This chapter describes the overview of bioinformatics; bioinformatics, data mining, and data visualization;

bioinformatics and secretome analysis; bioinformatics, mass spectrometry, and chemical cross-linking reagents; bioinformatics and Software Product Line (SPL); bioinformatics and protein kinase; bioinformatics and MicroRNAs (miRNAs); and clinical bioinformatics and cancer. Bioinformatics is the application of computer technology to the management and analysis of biological data. Bioinformatics is an interdisciplinary research area that is the interface between biology and computer science. The primary goal of bioinformatics is to reveal the wealth of biological information hidden in the large amounts of data and obtain a clearer insight into the fundamental biology of organisms. Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve the formal and practical problems arising from the management and analysis of biological data.

Chapter 3 Protein Structure Prediction .................................................................................................................. 48 Hirak Jyoti Chakraborty, Central Inland Fisheries Research Institute, India Aditi Gangopadhyay, Jhargram Raj College, India Sayak Ganguli, Amplicon Institute of Interdisciplinary Science and Technology, India Abhijit Datta, Jhargram Raj College, India The great disagreement between the number of known protein sequences and the number of experimentally

determined protein structures indicate an enormous necessity of rapid and accurate protein structure prediction methods. Computational techniques such as comparative modeling, threading and ab

initio modelling allow swift protein structure prediction with sufficient accuracy. The three phases of

computational protein structure prediction comprise: the pre-modelling analysis phase, model construction and post-modelling refinement. Protein modelling is primarily comparative or ab initio. Comparative or template-based methods such as homology and threading-based modelling require structural templates for constructing the structure of a target sequence. The ab initio is a template-free modelling approach which proceeds by satisfying various physics-based and knowledge-based parameters. The chapter will elaborate on the three phases of modelling, the programs available for performing each, issues, possible solutions and future research areas.

Chapter 4 Proteomics in Personalized Medicine: An Evolution Not a Revolution ............................................... 80 Srijan Goswami, Institute of Genetic Engineering, Badu, India The idea of personalized medicine system is an evolution of holistic approach of treatment and in more

evidence based manner. The chapter begins with an introduction of how body system works naturally and impact of modern medicine on overall health, followed by a historical background and brief review of literature providing the description that the concept of personalized medicine is not new but a very old ideology which stayed neglected until the development in the field of medical genetics, followed by the role of omics in modern medicine, the comparison of modern medicine and personalized medicine, medical concepts relevant to proteomics in personalized medicine, impact of proteomics in drug development and clinical safety and finally closing the chapter with future prospects and challenges of proteomics in personalized medicine.

Chapter 5 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage .......... 99 Sushma Munugala, Charles Sturt University, Australia Gagandeep K. Brar, Charles Sturt University, Australia Ali Syed, Charles Sturt University, Australia Azeem Mohammad, Charles Sturt University, Australia Malka N. Halgamuge, Charles Sturt University, Australia Cloud computing has shifted our old documents up into the clouds, with the advancement of technology. Fast-growing virtual document storage platforms provide amenities with minimal expense in the

corporate society. Despite living in the 20th century, even the first world countries have issues with the maintenance of document storage. Cloud computing resolves this issue for business and clinic owners as it banishes the requirement of planning, provisioning, and allows corporations to advance their filling system according to service demands. Medical practices heavily, rely on document storage as; almost all information contained in medical files is stored in a printed format. Medical practices urgently need to revolutionize their storage standards, to keep up with the growing population. The traditional method of paper storage in medical practice has completely been obsolete and needs to improve in order to assist patients with faster diagnosis in critical situations. Obtaining Knowledge and sharing it is an important part of medical practice, so it needs immediate attention to reach its full service potential. This chapter has analyzed content from literature that highlights issues regarding data storage and recommends solution. This inquiry has found a useful tool that can be beneficial for the development of this problem which is, ‘data mining’ as it gives the option of predictive, and preventative health care options, when medical data is searched. The functionality and worthiness of each algorithm and methods are also determined in this study. By using cloud and big data services to improve the analysis of medical data in network of regional health information system, has huge advancements that assure convenient management, easy extension, flexible investment, and low requirements for low technical based private medical units.

Section 2

Bioinformatics in the Fields of Genomics and Proteomics as Applied to Medicine, Health

Issues, and Medical Systems

This section covers topics related to computational systems biology, machine learning in protein fold

recognition, integrative data analysis, structural class prediction of protein, functional study of specific

molecular and pathway analysis, data mining in proteomics, homology detection and sequence alignment

methods, protein expression analysis, protein sequencing and classification, molecular dynamics

simulation, protein docking and drug design, homology detection and sequence alignment methods,

multiscale network construction. Environment and health issues as related to integration of big data

analytics with bioinformatics, integrated exposure modelling assimilation of exposure measurements

and human biomonitoring, data statistics and big data analytics for exposome-wide association studies.

This chapter provides a comprehensive overview of the state of the art and beyond regarding modelling and data analytics towards refined external and internal exposure assessment, for elucidating the human exposome. This includes methods for more accurate measurement of personal exposure (using wearable sensors) and for extrapolation to larger population groups (agent-based modelling). A key component in the modern risk and health impact assessment is the translation of external exposure into internal exposure metrics, accounting for age, gender, genetic and route of exposure dependent differences. The applicability of biokinetics covering a large chemical space is enhanced using quantitative structure activity relationships, especially when the latter are estimated using machine learning tools. Finally, comprehensive biomonitoring data interpretation and assimilation are supported by exposure reconstruction algorithms coupled with biokinetics

Chapter 7 Informatics and Data Analytics to Support Exposome-Based Discovery: Part 2 - Computational Exposure Biology................................................................................................................................ 145 Dimosthenis A. Sarigiannis, Aristotle University of Thessaloniki, Greece Alberto Gotti, Aristotle University of Thessaloniki, Greece Evangelos Handakas, Aristotle University of Thessaloniki, Greece Spyros P. Karakitsios, Aristotle University of Thessaloniki, Greece This chapter aims at outlining the current state of science in the field of computational exposure biology

and in particular at demonstrating how the bioinformatics techniques and algorithms can be used to support the association between environmental exposures and human health and the deciphering of the molecular and metabolic pathways of induced toxicity related to environmental chemical stressors. Examples of the integrated bioinformatics analyses outlined herein are given concerning exposure to airborne chemical mixtures, to organic compounds frequently found in consumer goods, and to mixtures of organic chemicals and metals through multiple exposure pathways. Advanced bioinformatics are coupled with big data analytics to perform studies of exposome-wide associations with putative adverse health outcomes. In conclusion, the chapter gives the reader an outline of the available computational tools and paves the way towards the development of future comprehensive applications that are expected to support efficiently exposome research in the 21st century.

Chapter 8 Transcriptomics to Metabolomics: A Network Perspective for Big Data ........................................... 188 Ankush Bansal, Jaypee University of Information Technology, India Pulkit Anupam Srivastava, Jaypee University of Information Technology, India A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics,

proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

Chapter 9 Protein Docking and Drug Design ...................................................................................................... 207 Aditi Gangopadhyay, Jhargram Raj College, India Hirak Jyoti Chakraborty, Central Inland Fisheries Research Institute, India Abhijit Datta, Department of Botany, Jhargram Raj College, India Protein docking is integral to structure-based drug design and molecular biology. The recent surge of

big data in biology, the demand for personalised medicines, evolving pathogens and increasing lifestyle- associated risks, asks for smart, robust, low-cost and high-throughput drug design. Computer-aided drug design techniques allow rapid screening of ultra-large chemical libraries within minutes. This is immensely necessary to the drug discovery pipeline, which is presently burdened with high attrition rates, failures, huge capital and time investment. With increasing drug resistance and difficult druggable targets, there is a growing need for novel drug scaffolds which is partly satisfied by fragment based drug design and de novo methods. The chapter discusses various aspects of protein docking and emphasises on its application in drug design.

Section 3

Big Data Analytics for Medical and Health informatics

This section covers topics related to health analytics and informatics, medical and health informatics

by using “-omics” data, system biology, disease control, predictive model of disease state, translational

medicine, drug design, combinatorial drug discovery, proteomics in personalized medicine, image

processing including medical imaging, healthcare and healthcare delivery, healthcare policy research,

healthcare outcomes research, monitoring and evaluation, hospital information system, Electronic

Medical Record and Electronic Health Record, population health management, decision support systems,

telemedicine, Human-Machine Interfaces, ICT, Ageing and Disability, Mobile technologies for Healthcare

applications (m-Health), Evaluation and use of Healthcare IT, Health Knowledge Management, Healthcare

Management and Information Systems, Software Systems in Medicine, Data Mining and Visualization,

Virtual Healthcare Teams, e-Health for Public Health integrating genetics with e-health.

Chapter 10 Effective and Efficient Business Intelligence Dashboard Design: Gestalt Theory in Dutch Long- Term and Chronic Healthcare ............................................................................................................. 243 Marco Spruit, Utrecht University, The Netherlands Max Lammertink, Utrecht University, The Netherlands This research focuses on the design process of an effective and efficient dashboard which displays

management information for an Electronic Health Record (EHR) in Dutch long-term and chronic healthcare. It presents the actual design and realization of a management dashboard for the YBoard 2.0 system, which is a popular solution on the Dutch market. The design decisions in this investigation were based on human perception and computer interaction theory, in particular Gestalt theory. The empirical interviews with medical professionals supplemented valuable additional insights into what the users wanted to see most of all in a dashboard in their daily practices. This study successfully shows how effective and efficient dashboard design can benefit from theoretical insights related to human perception and computer interaction such as Gestalt theory, in combination with integrated end user requirements from daily practices.

Chapter 11 Role of Online Data from Search Engine and Social Media in Healthcare Informatics..................... 272 M. Saqib Nawaz, Peking University, China Raza Ul Mustafa, COMSATS Institute of IT, Sahiwal, Pakistan M. Ikram Ullah Lali, University of Sargodha, Pakistan Search engines and social media are two different online data sources where search engines can provide

health related queries logs and Internet users’ discuss their diseases, symptoms, causes, preventions and even suggest treatment by sharing their views, experiences and opinions on social media. This chapter hypothesizes that online data from Google and Twitter can provide vital first-hand healthcare information. An approach is provided for collecting twitter data by exploring contextual information gleaned from Google search queries logs. Furthermore, it is investigated that whether it is possible to use tweets to track, monitor and predict diseases, especially Influenza epidemics. Obtained results show that healthcare institutes and professional’s uses social media to provide up-to date health related information and interact with public. Moreover, proposed approach is beneficial for extracting useful information regarding disease symptoms, side effects, medications and to track geographical location of epidemics affected area.

machine learning domain beyond the scope of supervised learning from data. Medical diagnostic process works mostly in supervised mode, but in reality, we are in the presence of a large amount of unlabeled samples and a small set of labeled examples characterized by thousands of features. This problem is known under the term “the curse of dimensionality”. In this study, we propose, as solution, a new approach in semi-supervised learning that we would call Optim Co-forest. The Optim Co-forest algorithm combines the re-sampling data approach with two selection strategies. The first one involves selecting random subset of parameters to construct the ensemble of classifiers following the principle of Co-forest. The second strategy is an extension of the importance measure of Random Forest (RF). Experiments on high dimensional datasets confirm the power of the adopted selection strategies in the scalability of our method.

Chapter 13 Predicting Patterns in Hospital Admission Data ................................................................................. 322 Jesús Manuel Puentes Gutiérrez, Universidad de Alcalá, Spain Salvador Sánchez-Alonso, Universidad de Alcalá, Spain Miguel-Angel Sicilia, University of Alcalá, Spain Elena García Barriocanal, Universidad de Alcalá, Spain Predicting patterns to extract knowledge can be a tough task but it is worth. When you want to accomplish

that task you have to take your time analysing all the data you have and you have to adapt it to the algorithms and technologies you are going to use after analysing. So you need to know the type of data that you own. When you have finished making the analysis, you also need to know what you want to find out and, therefore, which methodologies you are going to use to accomplish your objectives. At the end of this chapter you can see a real case making all that process. In particular, a Classification problem is shown as an example when using machine learning methodologies to find out if a hospital patient should be admitted or not in Cardiology department.

Chapter 14 Selection of Pathway Markers for Cancer Using Collaborative Binary Multi-Swarm Optimization ....................................................................................................................................... 337 Prativa Agarwalla, Heritage Institute of Technology, India Sumitra Mukhopadhyay, Institute of Radiophysics and Electronics, India Pathway information for cancer detection helps to find co-regulated gene groups whose collective

expression is strongly associated with cancer development. In this paper, a collaborative multi-swarm binary particle swarm optimization (MS-BPSO) based gene selection technique is proposed that outperforms to identify the pathway marker genes. We have compared our proposed method with various statistical and pathway based gene selection techniques for different popular cancer datasets as well as a detailed comparative study is illustrated using different meta-heuristic algorithms like binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE), binary coded artificial bee colony (BABC) and genetic algorithm (GA). Experimental results show that the proposed MS-BPSO based method performs significantly better and the improved multi swarm concept generates a good subset of pathway markers which provides more effective insight to the gene-disease association with high accuracy and reliability.

Chapter 15 Applying Bayesian Networks in the Early Diagnosis of Bulimia and Anorexia Nervosa in Adolescents: Applying Bayesian Networks in Early Diagnosis in Adolescents ................................. 364 Placido Rogerio Pinheiro, University of Fortaleza, Brazil Mirian Caliope Dantas Pinheiro, University of Fortaleza, Brazil Victor Câmera Damasceno, University of Fortaleza, Brazil Marley Costa Marques, University of Fortaleza, Brazil Raquel Souza Bino Araújo, University of Fortaleza, Brazil Layane Mayara Gomes Castelo Branco, University of Fortaleza, Brazil The diseases and health problems are concerns of managers of the Unified Health System has costs

in more sophisticated care sector are high. The World Health Organization focused on prevention of chronic diseases to prevent millions of premature deaths in the coming years, bringing substantial gains in economic growth by improving the quality of life. Few countries appear to be aimed at prevention, if not note the available knowledge and control of chronic diseases and may represent an unnecessary risk to future generations. Early diagnosis of these diseases is the first step to successful treatment in any age group. The objective is to build a model, from the establishment of a Bayesian network, for the early diagnosis of nursing to identify eating disorders bulimia and anorexia nervosa in adolescents, from the characteristics of the DSM-IV and Nursing Diagnoses The need for greater investment in technology in public health actions aims to increase the knowledge of health professionals, especially nurses, contributing to prevention, decision making and early treatment of problems.

Chapter 16 Image Processing Including Medical Liver Imaging: Medical Image Processing from Big Data Perspective, Ultrasound Liver Images, Challenges ............................................................................. 380 Suganya Ramamoorthy, Thiagarajar College of Engineering, India Rajaram Sivasubramaniam, Thiagarajar College of Engineering, India Medical diagnosis has been gaining importance in everyday life. The diseases and their symptoms are

highly varying and there is always a need for a continuous update of knowledge needed for the doctors. The diseases fall into different categories and a small variation of symptoms may leave to different categories of diseases. This is further supplemented by the medical analysts for a continuous treatment process. The treatment generally starts with a diagnosis and further goes through a set of procedures including X-ray, CT-scans, ultrasound imaging for qualitative analysis and diagnosis by doctors. A small level of error in disease identification introduces overhead in diagnosis and difficult in treatment. In such cases, an automated system that could retrieve medical images based on user’s interest. This chapter deals with various techniques, methodologies that correspond to the classification problem in data analysis process and its methodological impacts to big data.

Compilation of References ............................................................................................................... 393

About the Contributors .................................................................................................................... 454

Index ................................................................................................................................................... 463 xvi Preface

The emerging advances of Bioinformatics and the need to improve Healthcare and the Management of Medical Systems has promoted research collaborations among researchers from the field of Bioin- formatics and Health Informatics together with administrators, clinicians and data scientists. There is increased need to improve target drug therapy, personalized medicine and the way clinical decisions are made for the welfare of patients and on the management of medical systems. These needs demand Big Data Analytics incorporating the latest computational intelligence and statistical methodologies together with Data Mining and Machine Learning Methodologies. Big Data aspects such as data volume, data velocity, data veracity and data value are considered and examined in terms of usefulness and importance as to which is the most decisive criterion turning data from big to smart as a real time assistance for the improvement of living conditions. The increased need to improve healthcare and the welfare of patients and people in general, requires that we fast improve prognosis, diagnosis and therapies in order to advance personalized medicine and targeted drug/gene therapy (Alyass, et al., 2016; Chen et al., 2013; Tenenbaum, 2016; Greene et al., 2013).

The overall scope and main objective of the book is to expose the reader to the latest developments in Bioinformatics and Health Informatics but to also put emphasis on increasing awareness of all stakehold- ers of the importance to move from a data management organizational culture to a learning organization culture. We believe that the carefully chosen individual chapters address effective ways of communication and dissemination of the biological relevance of genomic and proteomic discoveries and related specific gene expression to realizing the clinical potential to make possible targeted therapy, personalized medicine and enhancement of human wellness and social cohesion. The recent advances in Bioinformatics and in Healthcare Informatics for next generation medical research are examined with the goal to improve medical practices and the management of Medical Systems both at the national and global level. The book includes chapters on innovation of advanced methods and techniques from medicine bioinformatics/ health informatics as also from computer science, statistics, and information theory which infer the relationships and dynamics among genes and their products. Both the genes and their products could be targets for treatment but also help propose predictive models which will enable the industry to develop an array of algorithms and software and overall accurate and intelligent computational systems for next generation medicine and medical systems.

Preface

THE CHALLENGES

The rapid development of biomedical research has given rise to an increasing demand of various computational and mathematical approaches to analyze and integrate the resulting large-scale data with the molecular and bioinformatics basis of clinical science. The new approaches will provide useful therapeutic targets to improve diagnosis, therapies and prognosis of diseases but to also help toward the establishment of better and more efficient next generation medicine and medical systems.

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Chapter 1 Bioinformatics as Applied to Medicine: Challenges Faced Moving from Big Data to Smart Data

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