Artificial Intelligence in Bioinformatics

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

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  • Author : Mario Cannataro
  • Publisher : Elsevier
  • Pages : 250 pages
  • ISBN : 9780128229521
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKArtificial Intelligence in Bioinformatics

Artificial Intelligence in Bioinformatics

Artificial Intelligence in Bioinformatics
  • Author : Mario Cannataro,Pietro Hiram Guzzi,Giuseppe Agapito,Chiara Zucco,Marianna Milano
  • Publisher : Elsevier
  • Release : 15 April 2021
GET THIS BOOKArtificial Intelligence in Bioinformatics

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

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

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

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

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

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

Evolutionary Computation in Bioinformatics

Evolutionary Computation in Bioinformatics
  • Author : Gary B. Fogel,David W. Corne
  • Publisher : Morgan Kaufmann
  • Release : 20 October 2021
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.

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

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.

Bioinformatics

Bioinformatics
  • Author : Pierre Baldi,Professor Pierre Baldi,Søren Brunak,Francis Bach
  • Publisher : MIT Press
  • Release : 20 October 2021
GET THIS BOOKBioinformatics

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,

Kernel-based Data Fusion for Machine Learning

Kernel-based Data Fusion for Machine Learning
  • Author : Shi Yu,Léon-Charles Tranchevent,Bart Moor,Yves Moreau
  • Publisher : Springer
  • Release : 29 March 2011
GET THIS BOOKKernel-based Data Fusion for Machine Learning

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

Artificial Intelligence and Heuristic Methods in Bioinformatics

Artificial Intelligence and Heuristic Methods in Bioinformatics
  • Author : Paolo Frasconi,Ron Shamir
  • Publisher : NATO Science Series: Computer & Systems Sciences
  • Release : 20 October 2021
GET THIS BOOKArtificial Intelligence and Heuristic Methods in Bioinformatics

This work focuses on methods and algorithms developed within the artificial intelligence community. These include machine learning, data mining, and pattern recognition. These methods provide solutions for the challenges posed by the progressive transformation of biology into data-massive science.

The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry
  • Author : Stephanie K. Ashenden
  • Publisher : Academic Press
  • Release : 07 May 2021
GET THIS BOOKThe Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows

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