Machine Learning In Bioinformatics

Author : Yanqing Zhang,Jagath C. Rajapakse
Publisher : John Wiley & Sons
Page : 476 pages
File Size : 52,9 Mb
Release : 2009-02-23
Category : Computers
ISBN : 9780470397411

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Machine Learning in Bioinformatics by Yanqing Zhang,Jagath C. Rajapakse Pdf

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.

Author : Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis
Publisher : CRC Press
Page : 386 pages
File Size : 52,9 Mb
Release : 2008-06-05
Category : Mathematics
ISBN : 9781420011784

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Introduction to Machine Learning and Bioinformatics by Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis Pdf

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.

Author : Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
Publisher : John Wiley & Sons
Page : 433 pages
File Size : 51,5 Mb
Release : 2021-01-20
Category : Computers
ISBN : 9781119785606

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Data Analytics in Bioinformatics by Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang Pdf

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.

Author : Pierre Baldi,Søren Brunak
Publisher : MIT Press (MA)
Page : 351 pages
File Size : 41,5 Mb
Release : 1998
Category : Biomolecules
ISBN : 026202442X

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Bioinformatics by Pierre Baldi,Søren Brunak Pdf

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible. In this book, 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.

Author : K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar
Publisher : Springer Nature
Page : 318 pages
File Size : 53,5 Mb
Release : 2020-01-30
Category : Technology & Engineering
ISBN : 9789811524455

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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications by K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar Pdf

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.

Author : Jason T. L. Wang,Mohammed J. Zaki,Hannu Toivonen,Dennis Shasha
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 44,7 Mb
Release : 2006-03-30
Category : Computers
ISBN : 9781846280597

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Data Mining in Bioinformatics by Jason T. L. Wang,Mohammed J. Zaki,Hannu Toivonen,Dennis Shasha Pdf

Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Author : Mario Cannataro,Pietro Hiram Guzzi,Giuseppe Agapito,Chiara Zucco,Marianna Milano
Publisher : Elsevier
Page : 270 pages
File Size : 48,9 Mb
Release : 2022-05-12
Category : Computers
ISBN : 9780128229293

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Artificial Intelligence in Bioinformatics by Mario Cannataro,Pietro Hiram Guzzi,Giuseppe Agapito,Chiara Zucco,Marianna Milano Pdf

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

Author : Loveleen Gaur,Arun Solanki,Samuel Fosso Wamba,Noor Zaman Jhanjhi
Publisher : CRC Press
Page : 220 pages
File Size : 45,5 Mb
Release : 2021-10-17
Category : Technology & Engineering
ISBN : 9781000463019

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Advanced AI Techniques and Applications in Bioinformatics by Loveleen Gaur,Arun Solanki,Samuel Fosso Wamba,Noor Zaman Jhanjhi Pdf

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

Author : Elena Marchiori
Publisher : Springer Science & Business Media
Page : 311 pages
File Size : 55,8 Mb
Release : 2007-04-02
Category : Computers
ISBN : 9783540717829

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics by Elena Marchiori Pdf

This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.

Author : Pierre Baldi,Søren Brunak
Publisher : MIT Press
Page : 492 pages
File Size : 45,9 Mb
Release : 2001-07-20
Category : Computers
ISBN : 026202506X

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Bioinformatics, second edition by Pierre Baldi,Søren Brunak Pdf

A guide to machine learning approaches and their application to the analysis of biological data. An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models—and to automate the process as much as possible. In this book Pierre Baldi and Søren 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 both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology. This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

Author : Shi Yu,Léon-Charles Tranchevent,Bart Moor,Yves Moreau
Publisher : Springer
Page : 214 pages
File Size : 50,5 Mb
Release : 2011-03-29
Category : Technology & Engineering
ISBN : 9783642194061

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Kernel-based Data Fusion for Machine Learning by Shi Yu,Léon-Charles Tranchevent,Bart Moor,Yves Moreau Pdf

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.

Author : Pablo Chamoso,Sara Rodríguez González,Mohd Saberi Mohamad,Alfonso González-Briones
Publisher : MDPI
Page : 206 pages
File Size : 52,9 Mb
Release : 2021-09-01
Category : Technology & Engineering
ISBN : 9783036507606

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Bioinformatics Applications Based On Machine Learning by Pablo Chamoso,Sara Rodríguez González,Mohd Saberi Mohamad,Alfonso González-Briones Pdf

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

Author : Lukasz Kurgan
Publisher : World Scientific
Page : 378 pages
File Size : 53,8 Mb
Release : 2022-12-06
Category : Science
ISBN : 9789811258596

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Machine Learning In Bioinformatics Of Protein Sequences: Algorithms, Databases And Resources For Modern Protein Bioinformatics by Lukasz Kurgan Pdf

Machine Learning in Bioinformatics of Protein Sequences guides readers around the rapidly advancing world of cutting-edge machine learning applications in the protein bioinformatics field. Edited by bioinformatics expert, Dr Lukasz Kurgan, and with contributions by a dozen of accomplished researchers, this book provides a holistic view of the structural bioinformatics by covering a broad spectrum of algorithms, databases and software resources for the efficient and accurate prediction and characterization of functional and structural aspects of proteins. It spotlights key advances which include deep neural networks, natural language processing-based sequence embedding and covers a wide range of predictions which comprise of tertiary structure, secondary structure, residue contacts, intrinsic disorder, protein, peptide and nucleic acids-binding sites, hotspots, post-translational modification sites, and protein function. This volume is loaded with practical information that identifies and describes leading predictive tools, useful databases, webservers, and modern software platforms for the development of novel predictive tools.

Author : Angelo Facchiano,Dominik Heider,Davide Chicco
Publisher : Frontiers Media SA
Page : 175 pages
File Size : 51,5 Mb
Release : 2020-06-18
Category : Electronic
ISBN : 9782889637522

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Artificial Intelligence Bioinformatics: Development and Application of Tools for Omics and Inter-Omics Studies by Angelo Facchiano,Dominik Heider,Davide Chicco Pdf

Author : A. Suresh,S. Vimal,Y. Harold Robinson,Dhinesh Kumar Ramaswami,R. Udendhran
Publisher : John Wiley & Sons
Page : 356 pages
File Size : 55,8 Mb
Release : 2022-03-24
Category : Computers
ISBN : 9781119792659

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Bioinformatics and Medical Applications by A. Suresh,S. Vimal,Y. Harold Robinson,Dhinesh Kumar Ramaswami,R. Udendhran Pdf

BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician’s important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.