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: 2021-04-01
ISBN 10: 0128229292
ISBN 13: 9780128229293
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Bioinformatics Book Review:

Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining reviews the main applications of the topic, from omics analysis to deep learning and network mining. The book includes a rigorous introduction on bioinformatics, also reviewing how methods are incorporated in tasks and processes. In addition, it presents methods and theory, including content for emergent fields such as Sentiment Analysis and Network Alignment. Other sections survey how Artificial Intelligence is exploited in bioinformatics applications, including sequence analysis, structure analysis, functional analysis, protein classification, omics analysis, biomarker discovery, integrative bioinformatics, protein interaction analysis, metabolic networks analysis, and much more. 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 on current trends and methods in a dynamic and growing field Provides academic teachers with a complete resource, covering fundamental concepts as well as applications

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.

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:

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.

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.

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.

Evolutionary Computation in Bioinformatics

Evolutionary Computation in Bioinformatics
Author: Gary B. Fogel,David W. Corne,Gary B.. Fogel
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.

Machine Learning Approaches to Bioinformatics

Machine Learning Approaches to Bioinformatics
Author: Anonim
Publsiher: Unknown
Total Pages: 329
Release: 2021
ISBN 10: 9814466786
ISBN 13: 9789814466783
Language: EN, FR, DE, ES & NL

Machine Learning Approaches to Bioinformatics Book Review:

Artificial Intelligence and Molecular Biology

Artificial Intelligence and Molecular Biology
Author: Lawrence Hunter
Publsiher: Aaai Press
Total Pages: 470
Release: 1993
ISBN 10:
ISBN 13: UOM:39015028911165
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Molecular Biology Book Review:

These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.

Application of Omics AI and Blockchain in Bioinformatics Research

Application of Omics  AI and Blockchain in Bioinformatics Research
Author: Jeffrey J. P. Tsai,Ka-Lok Ng
Publsiher: World Scientific Publishing Company
Total Pages: 208
Release: 2019
ISBN 10: 9789811203572
ISBN 13: 9811203571
Language: EN, FR, DE, ES & NL

Application of Omics AI and Blockchain in Bioinformatics Research Book Review:

With the increasing availability of omics data and mounting evidence of the usefulness of computational approaches to tackle multi-level data problems in bioinformatics and biomedical research in this post-genomics era, computational biology has been playing an increasingly important role in paving the way as basis for patient-centric healthcare. Two such areas are: (i) implementing AI algorithms supported by biomedical data would deliver significant benefits/improvements towards the goals of precision medicine (ii) blockchain technology will enable medical doctors to securely and privately build personal healthcare records, and identify the right therapeutic treatments and predict the progression of the diseases. A follow-up in the publication of our book Computation Methods with Applications in Bioinformatics Analysis (2017), topics in this volume include: clinical bioinformatics, omics-based data analysis, Artificial Intelligence (AI), blockchain, big data analytics, drug discovery, RNA-seq analysis, tensor decomposition and Boolean network.

Artificial Intelligence and Heuristic Methods in Bioinformatics

Artificial Intelligence and Heuristic Methods in Bioinformatics
Author: Paolo Frasconi,Ron Shamir
Publsiher: Unknown
Total Pages: 243
Release: 2003
ISBN 10:
ISBN 13: UOM:39015058787329
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Heuristic Methods in Bioinformatics Book Review:

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 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.

Artificial Intelligence Methods and Tools for Systems Biology

Artificial Intelligence Methods and Tools for Systems Biology
Author: W. Dubitzky,Francisco Azuaje
Publsiher: Springer
Total Pages: 221
Release: 2006-08-02
ISBN 10: 1402028652
ISBN 13: 9781402028656
Language: EN, FR, DE, ES & NL

Artificial Intelligence Methods and Tools for Systems Biology Book Review:

This book provides simultaneously a design blueprint, user guide, research agenda, and communication platform for current and future developments in artificial intelligence (AI) approaches to systems biology. It places an emphasis on the molecular dimension of life phenomena and in one chapter on anatomical and functional modeling of the brain. As design blueprint, the book is intended for scientists and other professionals tasked with developing and using AI technologies in the context of life sciences research. As a user guide, this volume addresses the requirements of researchers to gain a basic understanding of key AI methodologies for life sciences research. Its emphasis is not on an intricate mathematical treatment of the presented AI methodologies. Instead, it aims at providing the users with a clear understanding and practical know-how of the methods. As a research agenda, the book is intended for computer and life science students, teachers, researchers, and managers who want to understand the state of the art of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. Our aim was to maintain the readability and accessibility of a textbook throughout the chapters, rather than compiling a mere reference manual. The book is also intended as a communication platform seeking to bride the cultural and technological gap among key systems biology disciplines. To support this function, contributors have adopted a terminology and approach that appeal to audiences from different backgrounds.

Cognitive Science and Artificial Intelligence

Cognitive Science and Artificial Intelligence
Author: Sasikumar Gurumoorthy,Bangole Narendra Kumar Rao,Xiao-Zhi Gao
Publsiher: Springer
Total Pages: 112
Release: 2017-12-22
ISBN 10: 9811066981
ISBN 13: 9789811066986
Language: EN, FR, DE, ES & NL

Cognitive Science and Artificial Intelligence Book Review:

This book presents interdisciplinary research on cognition, mind and behavior from an information processing perspective. It includes chapters on Artificial Intelligence, Decision Support Systems, Machine Learning, Data Mining and Support Vector Machines, chiefly with regard to the data obtained and analyzed in Medical Informatics, Bioinformatics and related disciplines. The book reflects the state-of-the-art in Artificial Intelligence and Cognitive Science, and covers theory, algorithms, numerical simulation, error and uncertainty analysis, as well novel applications of new processing techniques in Biomedical Informatics, Computer Science and its applied areas. As such, it offers a valuable resource for students and researchers from the fields of Computer Science and Engineering in Medicine and Biology.

Artificial Intelligence in Precision Health

Artificial Intelligence in Precision Health
Author: Debmalya Barh
Publsiher: Academic Press
Total Pages: 544
Release: 2020-03-04
ISBN 10: 0128173386
ISBN 13: 9780128173381
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Precision Health Book Review:

Artificial Intelligence in Precision Health: From Concept to Applications provides a readily available resource to understand artificial intelligence and its real time applications in precision medicine in practice. Written by experts from different countries and with diverse background, the content encompasses accessible knowledge easily understandable for non-specialists in computer sciences. The book discusses topics such as cognitive computing and emotional intelligence, big data analysis, clinical decision support systems, deep learning, personal omics, digital health, predictive models, prediction of epidemics, drug discovery, precision nutrition and fitness. Additionally, there is a section dedicated to discuss and analyze AI products related to precision healthcare already available. This book is a valuable source for clinicians, healthcare workers, and researchers from diverse areas of biomedical field who may or may not have computational background and want to learn more about the innovative field of artificial intelligence for precision health. Provides computational approaches used in artificial intelligence easily understandable for non-computer specialists Gives know-how and real successful cases of artificial intelligence approaches in predictive models, modeling disease physiology, and public health surveillance Discusses the applicability of AI on multiple areas, such as drug discovery, clinical trials, radiology, surgery, patient care and clinical decision support

Python Programming for Biology

Python Programming for Biology
Author: Tim J. Stevens,Wayne Boucher
Publsiher: Cambridge University Press
Total Pages: 711
Release: 2015-02-12
ISBN 10: 0521895839
ISBN 13: 9780521895835
Language: EN, FR, DE, ES & NL

Python Programming for Biology Book Review:

This book introduces Python as a powerful tool for the investigation of problems in computational biology, for novices and experienced programmers alike.

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.

Molecular Bioinformatics

Molecular Bioinformatics
Author: Steffen Schulze-Kremer
Publsiher: Walter de Gruyter
Total Pages: 315
Release: 1996-01-01
ISBN 10: 3110808919
ISBN 13: 9783110808919
Language: EN, FR, DE, ES & NL

Molecular Bioinformatics Book Review: