This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

This book is designed to equip students to navigate through MS Excel and basic data computation methods, which are essential tools in research or professional settings and in classrooms. It illustrates the concepts used in research and data analysis and economic and financial decision-making in business and in daily life. The book will help students acquire knowledge and develop skills in statistical techniques and financial analysis using MS Excel. With illustrations and examples, it will help the readers to: Visualize, present, and analyze data through MS Excel spreadsheets and tables and create personal or business spreadsheets Learn how to work with spreadsheets, use formulae, and perform calculations and analysis Create tables including Pivot Tables Become familiar with basic statistical and financial measures Design creative spread sheets and communicate effectively in business using spreadsheets and data analysis This revised and updated second edition will be an essential resource for students of economics, commerce, management, and other social science subjects, and will be useful to those studying econometrics, financial technology, basic computational techniques, data analysis, and applied economics. Content The book is developed through three phases, with each phase standing on its own as well as providing a foundation for the next. In the first phase, Excel is introduced for the students to learn entry of data, manipulation of data, carrying out operations and develop presentations. The second phase introduces basic statistical measures of data summarisation and analysis, following which these are illustrated in Excel spreadsheets with the techniques introduced in the first phase. In addition, a few advanced tools of statistical analysis are introduced and illustrated in Excel. The third phase introduces financial measures of common use, their general computation and working them out in Excel. The book intends to illustrate the concepts used in economic and financial decision-making in business and in daily life; it helps demonstrate a deeper understanding from both theoretical and practical perspectives. An effort has been made to make the book student-friendly by using simple language and giving a number of illustrations in each chapter, solved in such a simple manner that they can be easily understood by the students. Practical questions have been included at the end of each chapter so that the students can independently solve them and test their understanding of the concepts and computations introduced in the chapter. Outcome At the end, students will be able to describe what a spreadsheet is and what Excel’s capabilities are and can work with elements that make up the structure of a worksheet. They will be able to work with spreadsheets and enter data in Excel, use formulae and calculations, and create tables, charts and pivot tables. They will be familiar with basic statistical and financial measures of general use. They will be able to do basic computations in statistics and finance in Excel. Students will acquire the capacity to create personal and/or business spreadsheets following current professional and/or industry standards. Their potential for critical thinking to design and create spreadsheets and communicate in a business setting using spreadsheet vocabulary will be enhanced. In the digital age, students necessarily need to know data, data sources and how to ‘dirty’ their hands with data. There can be no substitute to ‘talking through numbers’. The book introduces students to a variety of Indian and International data sources and teaches them how to import data-be it social, economic, financial and so on-to the Excel sheet. Once they master it, the data world is there for them to conquer! The educational background required for the student to understand the text is some basic English and Mathematics of school-leaving level. Some fl air for numbers will be an asset and for them it will be a breeze; others will have to make an effort but ample illustrations and practice questions make life simple, whether it is basic statistics or slightly intricate finance!

Programming; Data management and manipulation; Numerical computations; Linear models; Nonlinear models; Simulation of Random processes; Computational graphics.

Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.

This textbook integrates GIS, spatial analysis, and computational methods for solving real-world problems in various policy-relevant social science applications. Thoroughly updated, the third edition showcases the best practices of computational spatial social science and includes numerous case studies with step-by-step instructions in ArcGIS Pro and open-source platform KNIME. Readers sharpen their GIS skills by applying GIS techniques in detecting crime hotspots, measuring accessibility of primary care physicians, forecasting the impact of hospital closures on local community, or siting the best locations for business. FEATURES Fully updated using the latest version of ArcGIS Pro and open-source platform KNIME Features two brand-new chapters on agent-based modeling and big data analytics Provides newly automated tools for regionalization, functional region delineation, accessibility measures, planning for maximum equality in accessibility, and agent-based crime simulation Includes many compelling examples and real-world case studies related to social science, urban planning, and public policy Provides a website for downloading data and programs for implementing all case studies included in the book and the KNIME lab manual Intended for students taking upper-level undergraduate and graduate-level courses in quantitative geography, spatial analysis, and GIS applications, as well as researchers and professionals in fields such as geography, city and regional planning, crime analysis, public health, and public administration.

This multi-author contributed volume gives a comprehensive overview of recent progress in various vibrational spectroscopic techniques and chemometric methods and their applications in chemistry, biology and medicine. In order to meet the needs of readers, the book focuses on recent advances in technical development and potential exploitations of the theory, as well as the new applications of vibrational methods to problems of recent general interest that were difficult or even impossible to achieve in the not so distant past. Integrating vibrational spectroscopy and computational approaches serves as a handbook for people performing vibrational spectroscopy followed by chemometric analysis hence both experimental methods as well as procedures of recommended analysis are described. This volume is written for individuals who develop new methodologies and extend these applications to new realms of chemical and medicinal interest.

Computational Methods for Communication Science showcases the use of innovative computational methods in the study of communication. This book discusses the validity of using big data in communication science and showcases a number of new methods and applications in the fields of text and network analysis. Computational methods have the potential to greatly enhance the scientific study of communication because they allow us to move towards collaborative large-N studies of actual behavior in its social context. This requires us to develop new skills and infrastructure and meet the challenges of open, valid, reliable, and ethical "big data" research. This volume brings together a number of leading scholars in this emerging field, contributing to the increasing development and adaptation of computational methods in communication science. The chapters in this book were originally published as a special issue of the journal Communication Methods and Measures.

The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2017, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions. The papers of this volume are organized in topical sections on: Biomedical Signal Processing; Computational Methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, Algorithms and Apparatus; Modeling and Simulation of Life Systems; Data Driven Analysis; Image and Video Processing; Advanced Fuzzy and Neural Network Theory and Algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced Methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization Methods; Computational Methods for Sustainable Environment.

This book gathers the refereed proceedings of the Artificial Intelligence and Bioinspired Computational Methods Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Artificial intelligence and bioinspired computational methods now represent crucial areas of computer science research. The topics presented here reflect the current discussion on cutting-edge hybrid and bioinspired algorithms and their applications.