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.

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

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.

Artificial Intelligence and Heuristic Methods in Bioinformatics

Artificial Intelligence and Heuristic Methods in Bioinformatics
Author: Paolo Frasconi,Ron Shamir
Publsiher: Anonim
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:

Machine Learning Approaches to Bioinformatics

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

Machine Learning Approaches to Bioinformatics Book Review:

Application Of Omics Ai And Blockchain In Bioinformatics Research

Application Of Omics  Ai And Blockchain In Bioinformatics Research
Author: Tsai Jeffrey J P,Ng Ka-lok
Publsiher: World Scientific
Total Pages: 208
Release: 2019-10-14
ISBN 10: 9811203598
ISBN 13: 9789811203596
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.

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.

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 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.

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.

Computational Intelligence in Bioinformatics

Computational Intelligence in Bioinformatics
Author: Gary B. Fogel,David W. Corne,Yi Pan
Publsiher: John Wiley & Sons
Total Pages: 376
Release: 2007-12-10
ISBN 10: 9780470199084
ISBN 13: 0470199083
Language: EN, FR, DE, ES & NL

Computational Intelligence in Bioinformatics Book Review:

Combining biology, computer science, mathematics, and statistics, the field of bioinformatics has become a hot new discipline with profound impacts on all aspects of biology and industrial application. Now, Computational Intelligence in Bioinformatics offers an introduction to the topic, covering the most relevant and popular CI methods, while also encouraging the implementation of these methods to readers' research.

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.

Handbook of Research on Computational Intelligence Applications in Bioinformatics

Handbook of Research on Computational Intelligence Applications in Bioinformatics
Author: Dash, Sujata,Subudhi, Bidyadhar
Publsiher: IGI Global
Total Pages: 514
Release: 2016-06-20
ISBN 10: 1522504281
ISBN 13: 9781522504283
Language: EN, FR, DE, ES & NL

Handbook of Research on Computational Intelligence Applications in Bioinformatics Book Review:

Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings. The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.

Distributed Computing Artificial Intelligence Bioinformatics Soft Computing and Ambient Assisted Living

Distributed Computing  Artificial Intelligence  Bioinformatics  Soft Computing  and Ambient Assisted Living
Author: Sigeru Omatu,Miguel P. Rocha,Jose Bravo,Florentino Fdez Riverola,Emilio Corchado,Andrés Bustillo,Juan Manuel Corchado Rodríguez
Publsiher: Springer Science & Business Media
Total Pages: 1305
Release: 2009-06-08
ISBN 10: 3642024807
ISBN 13: 9783642024801
Language: EN, FR, DE, ES & NL

Distributed Computing Artificial Intelligence Bioinformatics Soft Computing and Ambient Assisted Living Book Review:

This book constitutes the refereed proceedings of the 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, held in Salamanca, Spain in June 2009. The 167 revised full papers presented together with 3 invited lectures were carefully reviewed and selected from over 230 submissions. The papers are organized in thematic sections on theoretical foundations and models; learning and adaptation; self-organizing networks, methods and applications; fuzzy systems; evolutionary computation and genetic algoritms; pattern recognition; formal languages in linguistics; agents and multi-agent on intelligent systems; brain-computer interfaces (bci); multiobjetive optimization; robotics; bioinformatics; biomedical applications; ambient assisted living (aal) and ambient intelligence (ai); other applications.

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.

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:

Computational Intelligence in Biomedicine and Bioinformatics

Computational Intelligence in Biomedicine and Bioinformatics
Author: Tomasz G. Smolinski,Mariofanna G. Milanova,Aboul-Ella Hassanien
Publsiher: Springer Science & Business Media
Total Pages: 432
Release: 2008-10-01
ISBN 10: 9783540707769
ISBN 13: 354070776X
Language: EN, FR, DE, ES & NL

Computational Intelligence in Biomedicine and Bioinformatics Book Review:

The purpose of this book is to provide an overview of state-of-the-art methodologies currently utilized for biomedicine and/or bioinformatics-oriented applications. Researchers working in these fields will learn new methods to help tackle their problems.

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.

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.

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.