Artificial Intelligence in Bioinformatics

Artificial Intelligence in Bioinformatics
Author: Mario Cannataro,Pietro Hiram Guzzi,Giuseppe Agapito,Chiara Zucco,Marianna Milano
Publsiher: Elsevier
Total Pages: 250
Release: 2021-04-15
ISBN 10: 9780128229521
ISBN 13: 0128229527
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Bioinformatics Book Review:

Artificial Intelligence is used in several application domains to solve problems with improved accuracy and speed. This work reviews the main applications of Artificial Intelligence in Bioinformatics, from omics analysis, to deep learning and network mining. A main need for a new resource in this area is related to the fact that Artificial Intelligence is mainly treated in computer science texts, where the main focus is on computational aspects. On the other hand, bioinformatics books focus mainly on bioinformatics key methods and only touch basic aspects of Artificial Intelligence, machine learning and data mining. To face those issues, the book combines a rigorous introduction of Artificial Intelligence methods in the context of bioinformatics with a deep and systematic review of how those methods are incorporated in bioinformatics tasks and processes. This book first recalls main methods and theory behind Artificial Intelligence, including emergent fields such as Sentiment Analysis and Network Alignment. It then surveys how Artificial Intelligence is exploited in main bioinformatics applications, including sequence analysis, structure analysis, functional analysis, protein classification, omics analysis, biomarker discovery, integrative bioinformatics, protein interaction analysis, metabolic networks analysis, network embedding, ontologies, text mining, reasoning in bioinformatics and explainable models in bioinformatics. Bridges the gap between computer science and bioinformatics, combining an introduction to Artificial Intelligence methods with a systematic review of its applications in the life sciences Brings readers up to speed with current trends and methods in a dynamic and growing field Provides academic teachers with a complete resource, covering fundamental concepts as well as applications

Advanced AI Techniques and Applications in Bioinformatics

Advanced AI Techniques and Applications in Bioinformatics
Author: Loveleen Gaur,Arun Solanki,Samuel Fosso Wamba,Noor Zaman Jhanjhi
Publsiher: CRC Press
Total Pages: 282
Release: 2021-10-18
ISBN 10: 100046301X
ISBN 13: 9781000463019
Language: EN, FR, DE, ES & NL

Advanced AI Techniques and Applications in Bioinformatics Book Review:

The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers

Intelligent Bioinformatics

Intelligent Bioinformatics
Author: Edward Keedwell,Ajit Narayanan
Publsiher: John Wiley & Sons
Total Pages: 294
Release: 2005-12-13
ISBN 10: 0470021764
ISBN 13: 9780470021767
Language: EN, FR, DE, ES & NL

Intelligent Bioinformatics Book Review:

Bioinformatics is contributing to some of the most important advances in medicine and biology. At the forefront of this exciting new subject are techniques known as artificial intelligence which are inspired by the way in which nature solves the problems it faces. This book provides a unique insight into the complex problems of bioinformatics and the innovative solutions which make up ‘intelligent bioinformatics’. Intelligent Bioinformatics requires only rudimentary knowledge of biology, bioinformatics or computer science and is aimed at interested readers regardless of discipline. Three introductory chapters on biology, bioinformatics and the complexities of search and optimisation equip the reader with the necessary knowledge to proceed through the remaining eight chapters, each of which is dedicated to an intelligent technique in bioinformatics. The book also contains many links to software and information available on the internet, in academic journals and beyond, making it an indispensable reference for the 'intelligent bioinformatician'. Intelligent Bioinformatics will appeal to all postgraduate students and researchers in bioinformatics and genomics as well as to computer scientists interested in these disciplines, and all natural scientists with large data sets to analyse.

Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics
Author: Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis
Publsiher: CRC Press
Total Pages: 384
Release: 2008-06-05
ISBN 10: 1420011782
ISBN 13: 9781420011784
Language: EN, FR, DE, ES & NL

Introduction to Machine Learning and Bioinformatics Book Review:

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

Bioinformatics

Bioinformatics
Author: Pierre Baldi,Professor Pierre Baldi,Søren Brunak,Francis Bach
Publsiher: MIT Press
Total Pages: 452
Release: 2001
ISBN 10: 9780262025065
ISBN 13: 026202506X
Language: EN, FR, DE, ES & NL

Bioinformatics Book Review:

Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

Data Analytics in Bioinformatics

Data Analytics in Bioinformatics
Author: Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
Publsiher: John Wiley & Sons
Total Pages: 544
Release: 2021-01-20
ISBN 10: 111978560X
ISBN 13: 9781119785606
Language: EN, FR, DE, ES & NL

Data Analytics in Bioinformatics Book Review:

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.

Machine Learning Approaches to Bioinformatics

Machine Learning Approaches to Bioinformatics
Author: Anonim
Publsiher: Unknown
Total Pages: 135
Release: 2021
ISBN 10: 9814466786
ISBN 13: 9789814466783
Language: EN, FR, DE, ES & NL

Machine Learning Approaches to Bioinformatics Book Review:

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics
Author: Yanqing Zhang,Jagath C. Rajapakse
Publsiher: John Wiley & Sons
Total Pages: 400
Release: 2009-02-23
ISBN 10: 9780470397411
ISBN 13: 0470397411
Language: EN, FR, DE, ES & NL

Machine Learning in Bioinformatics Book Review:

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

A Guided Tour of Artificial Intelligence Research

A Guided Tour of Artificial Intelligence Research
Author: Pierre Marquis,Odile Papini,Henri Prade
Publsiher: Springer Nature
Total Pages: 575
Release: 2020-05-08
ISBN 10: 3030061701
ISBN 13: 9783030061708
Language: EN, FR, DE, ES & NL

A Guided Tour of Artificial Intelligence Research Book Review:

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This third volume is dedicated to the interfaces of AI with various fields, with which strong links exist either at the methodological or at the applicative levels. The foreword of this volume reminds us that AI was born for a large part from cybernetics. Chapters are devoted to disciplines that are historically sisters of AI: natural language processing, pattern recognition and computer vision, and robotics. Also close and complementary to AI due to their direct links with information are databases, the semantic web, information retrieval and human-computer interaction. All these disciplines are privileged places for applications of AI methods. This is also the case for bioinformatics, biological modeling and computational neurosciences. The developments of AI have also led to a dialogue with theoretical computer science in particular regarding computability and complexity. Besides, AI research and findings have renewed philosophical and epistemological questions, while their cognitive validity raises questions to psychology. The volume also discusses some of the interactions between science and artistic creation in literature and in music. Lastly, an epilogue concludes the three volumes of this Guided Tour of AI Research by providing an overview of what has been achieved by AI, emphasizing AI as a science, and not just as an innovative technology, and trying to dispel some misunderstandings.

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques  Tools  and Applications
Author: K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar
Publsiher: Springer Nature
Total Pages: 317
Release: 2020-01-30
ISBN 10: 9811524459
ISBN 13: 9789811524455
Language: EN, FR, DE, ES & NL

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications Book Review:

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

Artificial Intelligence and Heuristic Methods in Bioinformatics

Artificial Intelligence and Heuristic Methods in Bioinformatics
Author: Paolo Frasconi,Ron Shamir
Publsiher: NATO Science Series: Computer & Systems Sciences
Total Pages: 243
Release: 2003
ISBN 10: 9781586032944
ISBN 13: 1586032941
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Heuristic Methods in Bioinformatics Book Review:

This work focuses on methods and algorithms developed within the artificial intelligence community. These include machine learning, data mining, and pattern recognition. These methods provide solutions for the challenges posed by the progressive transformation of biology into data-massive science.

Bioinformatics and RNA

Bioinformatics and RNA
Author: Dolly Sharma,Shailendra Singh,Mamta Mittal
Publsiher: CRC Press
Total Pages: 148
Release: 2021-08-18
ISBN 10: 1000428338
ISBN 13: 9781000428339
Language: EN, FR, DE, ES & NL

Bioinformatics and RNA Book Review:

This book offers a unique balance between a basic introductory knowledge of bioinformatics and a detailed study of algorithmic techniques. Bioinformatics and RNA: A Practice-Based Approach is a complete guide on the fundamental concepts, applications, algorithms, protocols, new trends, challenges, and research results in the area of bioinformatics and RNA. The book offers a broad introduction to the explosively growing new discipline of bioinformatics. It covers theoretical topics along with computational algorithms. It explores RNA bioinformatics, which contribute to therapeutics and drug discovery. Implementation of algorithms in a DotNet Framework with code and complete insight on the state-of-the-art and recent advancements are presented in detail. The book targets both novice readers as well as practitioners in the field. FEATURES Offers a broad introduction to the explosively growing new discipline of bioinformatics Covers theoretical topics and computational algorithms Explores RNA bioinformatics to unleash the potential from therapeutics to drug discovery Discusses implementation of algorithms in DotNet Frameworks with code Presents insights into the state of the art and recent advancements in bioinformatics The book is useful to undergraduate students with engineering, science, mathematics, or biology backgrounds. Researchers will be equally interested.

Bioinformatics Computing

Bioinformatics Computing
Author: Bryan P. Bergeron
Publsiher: Prentice Hall Professional
Total Pages: 439
Release: 2003
ISBN 10: 9780131008250
ISBN 13: 0131008250
Language: EN, FR, DE, ES & NL

Bioinformatics Computing Book Review:

Comprehensive and concise, this handbook has chapters on computing visualization, large database designs, advanced pattern matching and other key bioinformatics techniques. It is a practical guide to computing in the growing field of Bioinformatics--the study of how information is represented and transmitted in biological systems, starting at the molecular level.

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics
Author: Yi Pan,Jianxin Wang,Min Li
Publsiher: John Wiley & Sons
Total Pages: 536
Release: 2013-10-07
ISBN 10: 1118567811
ISBN 13: 9781118567814
Language: EN, FR, DE, ES & NL

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics Book Review:

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. Featuring a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics: Highlights protein analysis applications such as protein-related drug activity comparison Incorporates salient case studies illustrating how to apply the methods outlined in the book Tackles the complex relationship between proteins from a systems biology point of view Relates the topic to other emerging technologies such as data mining and visualization Includes many tables and illustrations demonstrating concepts and performance figures Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.

Evolutionary Computation in Bioinformatics

Evolutionary Computation in Bioinformatics
Author: Gary B. Fogel,David W. Corne
Publsiher: Morgan Kaufmann
Total Pages: 393
Release: 2003
ISBN 10: 9781558607972
ISBN 13: 1558607978
Language: EN, FR, DE, ES & NL

Evolutionary Computation in Bioinformatics Book Review:

This book offers a definitive resource that bridges biology and evolutionary computation. The authors have written an introduction to biology and bioinformatics for computer scientists, plus an introduction to evolutionary computation for biologists and for computer scientists unfamiliar with these techniques.

Kernel based Data Fusion for Machine Learning

Kernel based Data Fusion for Machine Learning
Author: Shi Yu,Léon-Charles Tranchevent,Bart Moor,Yves Moreau
Publsiher: Springer
Total Pages: 214
Release: 2011-03-29
ISBN 10: 3642194060
ISBN 13: 9783642194061
Language: EN, FR, DE, ES & NL

Kernel based Data Fusion for Machine Learning Book Review:

Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.

https www frontiersin org research topics 9029 artificial intelligence bioinformatics development and application of tools for omics and inter omic

https   www frontiersin org research topics 9029 artificial intelligence bioinformatics development and application of tools for omics and inter omic
Author: Angelo Facchiano,Dominik Heider,Davide Chicco
Publsiher: Frontiers Media SA
Total Pages: 175
Release: 2020-06-18
ISBN 10: 2889637522
ISBN 13: 9782889637522
Language: EN, FR, DE, ES & NL

https www frontiersin org research topics 9029 artificial intelligence bioinformatics development and application of tools for omics and inter omic Book Review:

The Era of Artificial Intelligence Machine Learning and Data Science in the Pharmaceutical Industry

The Era of Artificial Intelligence  Machine Learning  and Data Science in the Pharmaceutical Industry
Author: Stephanie K. Ashenden
Publsiher: Academic Press
Total Pages: 264
Release: 2021-05-07
ISBN 10: 0128204494
ISBN 13: 9780128204498
Language: EN, FR, DE, ES & NL

The Era of Artificial Intelligence Machine Learning and Data Science in the Pharmaceutical Industry Book Review:

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient’s life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide

Advances in Artificial Intelligence Computation and Data Science

Advances in Artificial Intelligence  Computation  and Data Science
Author: Tuan D. Pham,Hong Yan,Muhammad W. Ashraf,Folke Sjöberg
Publsiher: Springer Nature
Total Pages: 135
Release: 2021
ISBN 10: 303069951X
ISBN 13: 9783030699512
Language: EN, FR, DE, ES & NL

Advances in Artificial Intelligence Computation and Data Science Book Review:

Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity-in both time and memory requirements-or machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education. Tuan D. Pham is professor and founding director of the Center for Artificial Intelligence at Prince Mohammad Bin Fahd University, Saudi Arabia. Hong Yan is currently chair professor of computer engineering at City University of Hong Kong. Dr. Muhammad Waqar Ashraf is professor and dean of College of Sciences & Human Studies at Prince Mohammad Bin Fahd University. Folke Sjöberg is professor of burn surgery and critical care at Linköping University, Sweden, and director of the Burn Center at the Linköping University Hospital.

Encyclopedia of Bioinformatics and Computational Biology

Encyclopedia of Bioinformatics and Computational Biology
Author: Anonim
Publsiher: Elsevier
Total Pages: 3284
Release: 2018-08-21
ISBN 10: 0128114320
ISBN 13: 9780128114322
Language: EN, FR, DE, ES & NL

Encyclopedia of Bioinformatics and Computational Biology Book Review:

Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases