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Artificial Intelligence in Bioinformatics

Artificial Intelligence in Bioinformatics
  • Author : Mario Cannataro
  • Publsiher : Elsevier
  • Release : 25 May 2022
  • ISBN : 0128229292
  • Pages : 268 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKArtificial Intelligence in Bioinformatics

Summary:
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


Artificial Intelligence in Bioinformatics

Artificial Intelligence in Bioinformatics
  • Author : Mario Cannataro,Pietro Hiram Guzzi,Giuseppe Agapito,Chiara Zucco,Marianna Milano
  • Publisher : Elsevier
  • Release : 25 May 2022
GET THIS BOOKArtificial Intelligence in Bioinformatics

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,

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Advanced AI Techniques and Applications in Bioinformatics

Advanced AI Techniques and Applications in Bioinformatics
  • Author : Loveleen Gaur,Arun Solanki,Samuel Fosso Wamba,Noor Zaman Jhanjhi
  • Publisher : CRC Press
  • Release : 18 October 2021
GET THIS BOOKAdvanced AI Techniques and Applications in Bioinformatics

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

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Biomedical Data Mining for Information Retrieval

Biomedical Data Mining for Information Retrieval
  • Author : Subhendu Kumar Pani,Sujata Dash,S. Balamurugan,Ajith Abraham
  • Publisher : John Wiley & Sons
  • Release : 06 August 2021
GET THIS BOOKBiomedical Data Mining for Information Retrieval

This book comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including

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Data Analytics in Bioinformatics

Data Analytics in Bioinformatics
  • Author : Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
  • Publisher : John Wiley & Sons
  • Release : 20 January 2021
GET THIS BOOKData Analytics in Bioinformatics

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

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Intelligent Bioinformatics

Intelligent Bioinformatics
  • Author : Edward Keedwell,Ajit Narayanan
  • Publisher : John Wiley & Sons
  • Release : 13 December 2005
GET THIS BOOKIntelligent Bioinformatics

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

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A Guided Tour of Artificial Intelligence Research

A Guided Tour of Artificial Intelligence Research
  • Author : Pierre Marquis,Odile Papini,Henri Prade
  • Publisher : Springer Nature
  • Release : 08 May 2020
GET THIS BOOKA Guided Tour of Artificial Intelligence Research

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

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Advances in Bioinformatics

Advances in Bioinformatics
  • Author : Vijai Singh,Ajay Kumar
  • Publisher : Springer Nature
  • Release : 31 July 2021
GET THIS BOOKAdvances in Bioinformatics

This book presents the latest developments in bioinformatics, highlighting the importance of bioinformatics in genomics, transcriptomics, metabolism and cheminformatics analysis, as well as in drug discovery and development. It covers tools, data mining and analysis, protein analysis, computational vaccine, and drug design. Covering cheminformatics, computational evolutionary biology and the role of next-generation sequencing and neural network analysis, it also discusses the use of bioinformatics tools in the development of precision medicine. This book offers a valuable source of information for

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Evolutionary Computation in Bioinformatics

Evolutionary Computation in Bioinformatics
  • Author : Gary B. Fogel,David W. Corne
  • Publisher : Morgan Kaufmann
  • Release : 22 May 2022
GET THIS BOOKEvolutionary Computation in Bioinformatics

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.

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Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics
  • Author : Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis
  • Publisher : CRC Press
  • Release : 05 June 2008
GET THIS BOOKIntroduction to Machine Learning and Bioinformatics

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

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Machine Learning in Bioinformatics

Machine Learning in Bioinformatics
  • Author : Yanqing Zhang,Jagath C. Rajapakse
  • Publisher : John Wiley & Sons
  • Release : 23 February 2009
GET THIS BOOKMachine Learning in Bioinformatics

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

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
  • Author : Elena Marchiori
  • Publisher : Springer Science & Business Media
  • Release : 02 April 2007
GET THIS BOOKEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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 in April 2007, colocated with the Evo* 2007 events. The 28 revised full papers were carefully reviewed and selected from 60 submissions. Bringing experts in computer science together with experts in bioinformatics and the biological sciences resulted in contributions on fundamental and theoretical issues, along with a wide variety of papers dealing with different applications areas, such as

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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
  • Publisher : Springer Nature
  • Release : 30 January 2020
GET THIS BOOKStatistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

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.

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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
  • Publisher : World Scientific
  • Release : 14 October 2019
GET THIS BOOKApplication Of Omics, Ai And Blockchain In Bioinformatics Research

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

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Artificial Intelligence and Machine Learning in Healthcare

Artificial Intelligence and Machine Learning in Healthcare
  • Author : Ankur Saxena,Shivani Chandra
  • Publisher : Springer Nature
  • Release : 06 May 2021
GET THIS BOOKArtificial Intelligence and Machine Learning in Healthcare

This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed

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