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Intelligent Bioinformatics

Intelligent Bioinformatics
The Application of Artificial Intelligence Techniques to Bioinformatics Problems

by Edward Keedwell,Ajit Narayanan

  • Publisher : John Wiley & Sons
  • Release : 2005-12-13
  • Pages : 294
  • ISBN : 0470021764
  • Language : En, Es, Fr & De
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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.

Bioinformatics

Bioinformatics
The Machine Learning Approach

by Pierre Baldi,Professor Pierre Baldi,Søren Brunak,Francis Bach

  • Publisher : MIT Press
  • Release : 2001
  • Pages : 452
  • ISBN : 9780262025065
  • Language : En, Es, Fr & De
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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.

Application of Omics, AI and Blockchain in Bioinformatics Research

Application of Omics, AI and Blockchain in Bioinformatics Research
A Book

by Jeffrey J. P. Tsai,Ka-Lok Ng

  • Publisher : World Scientific Publishing Company
  • Release : 2019
  • Pages : 208
  • ISBN : 9789811203572
  • Language : En, Es, Fr & De
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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.

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

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications
A Book

by K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar

  • Publisher : Springer Nature
  • Release : 2020-01-30
  • Pages : 317
  • ISBN : 9811524459
  • Language : En, Es, Fr & De
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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.

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics
A Book

by Yanqing Zhang,Jagath C. Rajapakse

  • Publisher : John Wiley & Sons
  • Release : 2009-02-23
  • Pages : 400
  • ISBN : 9780470397411
  • Language : En, Es, Fr & De
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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.

Machine Learning Approaches to Bioinformatics

Machine Learning Approaches to Bioinformatics
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2021
  • Pages : 329
  • ISBN : 9814466786
  • Language : En, Es, Fr & De
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Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics
A Book

by Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis

  • Publisher : CRC Press
  • Release : 2008-06-05
  • Pages : 384
  • ISBN : 1420011782
  • Language : En, Es, Fr & De
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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
A Book

by Bryan P. Bergeron

  • Publisher : Prentice Hall Professional
  • Release : 2003
  • Pages : 439
  • ISBN : 9780131008250
  • Language : En, Es, Fr & De
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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 and Molecular Biology

Artificial Intelligence and Molecular Biology
A Book

by Lawrence Hunter

  • Publisher : Aaai Press
  • Release : 1993
  • Pages : 470
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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.

Evolutionary Computation in Bioinformatics

Evolutionary Computation in Bioinformatics
A Book

by Gary B. Fogel,David W. Corne,Gary B.. Fogel

  • Publisher : Morgan Kaufmann
  • Release : 2003
  • Pages : 393
  • ISBN : 9781558607972
  • Language : En, Es, Fr & De
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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.

Artificial Intelligence and Heuristic Methods in Bioinformatics

Artificial Intelligence and Heuristic Methods in Bioinformatics
A Book

by Paolo Frasconi,Ron Shamir

  • Publisher : John Wiley & Sons
  • Release : 2003
  • Pages : 243
  • ISBN : 9781586032944
  • Language : En, Es, Fr & De
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Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living

Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings

by Sigeru Omatu,Miguel P. Rocha,Jose Bravo,Florentino Fdez Riverola,Emilio Corchado,Andrés Bustillo,Juan Manuel Corchado Rodríguez

  • Publisher : Springer Science & Business Media
  • Release : 2009-06-08
  • Pages : 1305
  • ISBN : 3642024807
  • Language : En, Es, Fr & De
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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.

Artificial Intelligence Methods and Tools for Systems Biology

Artificial Intelligence Methods and Tools for Systems Biology
A Book

by W. Dubitzky,Francisco Azuaje

  • Publisher : Springer Science & Business Media
  • Release : 2005-01-05
  • Pages : 221
  • ISBN : 9781402029592
  • Language : En, Es, Fr & De
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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.

Kernel-based Data Fusion for Machine Learning

Kernel-based Data Fusion for Machine Learning
Methods and Applications in Bioinformatics and Text Mining

by Shi Yu,Léon-Charles Tranchevent,Bart Moor,Yves Moreau

  • Publisher : Springer
  • Release : 2011-03-29
  • Pages : 214
  • ISBN : 3642194060
  • Language : En, Es, Fr & De
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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.

Python Programming for Biology

Python Programming for Biology
A Book

by Tim J. Stevens,Wayne Boucher

  • Publisher : Cambridge University Press
  • Release : 2015-02-12
  • Pages : 711
  • ISBN : 0521895839
  • Language : En, Es, Fr & De
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This book introduces Python as a powerful tool for the investigation of problems in computational biology, for novices and experienced programmers alike.

Handbook of Research on Computational Intelligence Applications in Bioinformatics

Handbook of Research on Computational Intelligence Applications in Bioinformatics
A Book

by Dash, Sujata,Subudhi, Bidyadhar

  • Publisher : IGI Global
  • Release : 2016-06-20
  • Pages : 514
  • ISBN : 1522504281
  • Language : En, Es, Fr & De
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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.

Scalable Pattern Recognition Algorithms

Scalable Pattern Recognition Algorithms
Applications in Computational Biology and Bioinformatics

by Pradipta Maji,Sushmita Paul

  • Publisher : Springer Science & Business Media
  • Release : 2014-03-19
  • Pages : 304
  • ISBN : 3319056301
  • Language : En, Es, Fr & De
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This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

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
A Book

by Angelo Facchiano,Dominik Heider,Davide Chicco

  • Publisher : Frontiers Media SA
  • Release : 2020-06-18
  • Pages : 175
  • ISBN : 2889637522
  • Language : En, Es, Fr & De
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Bioinformatics Algorithms

Bioinformatics Algorithms
Techniques and Applications

by Ion Mandoiu,Alexander Zelikovsky

  • Publisher : John Wiley & Sons
  • Release : 2008-02-25
  • Pages : 528
  • ISBN : 0470097736
  • Language : En, Es, Fr & De
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Presents algorithmic techniques for solving problems in bioinformatics, including applications that shed new light on molecular biology This book introduces algorithmic techniques in bioinformatics, emphasizing their application to solving novel problems in post-genomic molecular biology. Beginning with a thought-provoking discussion on the role of algorithms in twenty-first-century bioinformatics education, Bioinformatics Algorithms covers: General algorithmic techniques, including dynamic programming, graph-theoretical methods, hidden Markov models, the fast Fourier transform, seeding, and approximation algorithms Algorithms and tools for genome and sequence analysis, including formal and approximate models for gene clusters, advanced algorithms for non-overlapping local alignments and genome tilings, multiplex PCR primer set selection, and sequence/network motif finding Microarray design and analysis, including algorithms for microarray physical design, missing value imputation, and meta-analysis of gene expression data Algorithmic issues arising in the analysis of genetic variation across human population, including computational inference of haplotypes from genotype data and disease association search in case/control epidemiologic studies Algorithmic approaches in structural and systems biology, including topological and structural classification in biochemistry, and prediction of protein-protein and domain-domain interactions Each chapter begins with a self-contained introduction to a computational problem; continues with a brief review of the existing literature on the subject and an in-depth description of recent algorithmic and methodological developments; and concludes with a brief experimental study and a discussion of open research challenges. This clear and approachable presentation makes the book appropriate for researchers, practitioners, and graduate students alike.

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics
A Book

by Yi Pan,Jianxin Wang,Min Li

  • Publisher : John Wiley & Sons
  • Release : 2013-10-07
  • Pages : 536
  • ISBN : 1118567811
  • Language : En, Es, Fr & De
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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.