Neuware - Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR.

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Detalles bibliogr�ficos

T�tulo: Data Science for Genomics
Editor: Elsevier Inc Dez 2022
Fecha de publicaci�n: 2022
Idioma: Ingl�s
ISBN 10: 0323983529
ISBN 13: 9780323983525
Encuadernaci�n: Taschenbuch
Estado: Neu
Dimensiones: 279�mil�metros de ancho por 216 de alto por 17 de largo
Peso: 733 gramos

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Sinopsis

Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes.

Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR.

Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics
Presents a roadmap of future trends suitable for innovative Data Science research and practice
Includes topics such as Blockchain technology for securing data at end user/server side
Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns

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Amit Kumar Tyagi is an Assistant Professor, at the National Forensic Sciences University, Gandhinagar, Gujarat, India. Previously he worked as an Assistant Professor (Senior Grade 2), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India from 2019-2022. He received his Ph.D. Degree (Full-Time) in 2018 from Pondicherry Central University, India. He joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) from 2009 to 2010, and 2012 to 2013. He was an Assistant Professor and head researcher at Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), India from 2018 to 2019. He supervised one PhD thesis and more than ten Master dissertations. He has contributed to several projects such as “AARIN� and “P3- Block� to address some of the open issues related to privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber-Physical Systems (MCPS). He has published over 200 papers in refereed high-impact journals, conferences, and books, and some of his articles won best paper awards. Also, he has filed more than 25 patents (Nationally and Internationally) in the areas of Deep Learning, Internet of Things, Cyber-Physical Systems, and Computer Vision. He has edited more than 25 books for IET, Elsevier, Springer, CRC Press, etc. Additionally, he has authored 4 Books on Intelligent Transportation Systems, Vehicular Ad-hoc Network, Machine learning and Internet of Things, with IET UK, Springer Germany, and BPB India publisher. He won the Faculty Research Award of the Year for 2020, 2021, and 2022 consecutively, given by Vellore Institute of Technology, Chennai, India. Recently, he was awarded the best paper award for his paper “A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient�, in ICCSA 2020, Italy (Europe). His current research focuses on Next Generation Machine Based Communications, Blockchain Technology, Smart and Secure Computing and Privacy. He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, Universal Scientific Education and Research Network, CSI, and ISTE.

Dr. Ajith Abraham is the Vice Chancellor at Sai University, Chennai. Before joining Sai University, he held the position of vice chancellor at prominent institutions and was also the founding director of Machine Intelligence Research Labs (MIR Labs), a non-profit scientific network for innovation and research excellence with headquarters in Seattle, USA. Dr. Abraham has completed research projects valued at over $110 million as an investigator or co-investigator from the United States, the European Union, Italy, the Czech Republic, France, Malaysia, China, and Australia. He has worked in a multidisciplinary setting for more than 35 years and has authored or co-authored more than 1,500+ research publications in artificial intelligence and related applications in the industry. A handful of his publications have been translated into Chinese and Russian, and one of his books has been translated into Japanese. The Scopus database has approximately 1,400 papers indexed, whereas the Thomson Web of Science has over 1,000 publications indexed.

In addition to other esteemed universities, Dr. Abraham has worked with researchers from MIT (USA), the University of Cambridge (UK), Harvard University (USA), and Oxford University (UK). According to Google Scholar, Dr. Abraham possesses over 63,000 scholarly citations with an H-index of over 118. He has delivered over 250 conference plenary talks and tutorials in more than 20 countries. From 2008 to 2021, Dr. Abraham chaired the IEEE Systems, Man, and Cybernetics Society Technical Committee on Soft Computing, which had more than 200 members. From 2011 to 2013, he represented Europe as a Distinguished Lecturer for the IEEE Computer Society (USA). Dr. Abraham is continuously listed in the Stanford/Elsevier list, highlighting the top 2% of the most cited scientists across the globe. Based on 2024 data, ScholarGPS listed Dr. Abraham as one of the world’s top 0.01% cited scientists in the engineering and computer science fields.

From 2016 to 2021, Dr. Abraham worked as the chief editor of Engineering Applications of Artificial Intelligence (EAAI) at Elsevier, New York. EAAI is one of the oldest journals (founded in 1988) in the artificial intelligencedomain. Additionally, he sat on the editorial boards of more than 15 international journals indexed by Thomson ISI. Dr. Abraham received his Ph.D. degree in artificial intelligence from Monash University, Melbourne, Australia (2001), a Master of Science degree from Nanyang Technological University, Singapore (1998), and a B.Tech (Hons) degree from the University of Calicut in 1990.

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