Artificial Intelligence in Bioinformatics

Artificial Intelligence in Bioinformatics
Author: Mario Cannataro,Pietro Hiram Guzzi,Giuseppe Agapito,Chiara Zucco,Marianna Milano
Publsiher: Elsevier
Total Pages: 268
Release: 2022-05-25
ISBN 10: 0128229292
ISBN 13: 9780128229293
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Bioinformatics Book Review:

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

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
Publsiher: CRC Press
Total Pages: 282
Release: 2021-10-18
ISBN 10: 100046301X
ISBN 13: 9781000463019
Language: EN, FR, DE, ES & NL

Advanced AI Techniques and Applications in Bioinformatics Book Review:

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

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.

Data Analytics in Bioinformatics

Data Analytics in Bioinformatics
Author: Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
Publsiher: John Wiley & Sons
Total Pages: 544
Release: 2021-01-20
ISBN 10: 111978560X
ISBN 13: 9781119785606
Language: EN, FR, DE, ES & NL

Data Analytics in Bioinformatics Book Review:

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.

Biomedical Data Mining for Information Retrieval

Biomedical Data Mining for Information Retrieval
Author: Subhendu Kumar Pani,Sujata Dash,S. Balamurugan,Ajith Abraham
Publsiher: John Wiley & Sons
Total Pages: 448
Release: 2021-08-06
ISBN 10: 1119711266
ISBN 13: 9781119711261
Language: EN, FR, DE, ES & NL

Biomedical Data Mining for Information Retrieval Book Review:

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 patient's data, electronic health records (EHRs) and lifestyle. Previously it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical Image Mining, a novel research area, due to its large amount of biomedical images increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.

A Guided Tour of Artificial Intelligence Research

A Guided Tour of Artificial Intelligence Research
Author: Pierre Marquis,Odile Papini,Henri Prade
Publsiher: Springer Nature
Total Pages: 575
Release: 2020-05-08
ISBN 10: 3030061701
ISBN 13: 9783030061708
Language: EN, FR, DE, ES & NL

A Guided Tour of Artificial Intelligence Research Book Review:

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 foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This third volume is dedicated to the interfaces of AI with various fields, with which strong links exist either at the methodological or at the applicative levels. The foreword of this volume reminds us that AI was born for a large part from cybernetics. Chapters are devoted to disciplines that are historically sisters of AI: natural language processing, pattern recognition and computer vision, and robotics. Also close and complementary to AI due to their direct links with information are databases, the semantic web, information retrieval and human-computer interaction. All these disciplines are privileged places for applications of AI methods. This is also the case for bioinformatics, biological modeling and computational neurosciences. The developments of AI have also led to a dialogue with theoretical computer science in particular regarding computability and complexity. Besides, AI research and findings have renewed philosophical and epistemological questions, while their cognitive validity raises questions to psychology. The volume also discusses some of the interactions between science and artistic creation in literature and in music. Lastly, an epilogue concludes the three volumes of this Guided Tour of AI Research by providing an overview of what has been achieved by AI, emphasizing AI as a science, and not just as an innovative technology, and trying to dispel some misunderstandings.

Machine Learning Approaches to Bioinformatics

Machine Learning Approaches to Bioinformatics
Author: Anonim
Publsiher: Unknown
Total Pages: 135
Release: 2022
ISBN 10: 9814466786
ISBN 13: 9789814466783
Language: EN, FR, DE, ES & NL

Machine Learning Approaches to Bioinformatics Book Review:

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.

Advances in Bioinformatics

Advances in Bioinformatics
Author: Vijai Singh,Ajay Kumar
Publsiher: Springer Nature
Total Pages: 446
Release: 2021-07-31
ISBN 10: 9813361913
ISBN 13: 9789813361911
Language: EN, FR, DE, ES & NL

Advances in Bioinformatics Book Review:

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 not only beginners in bioinformatics, but also for students, researchers, scientists, clinicians, practitioners, policymakers, and stakeholders who are interested in harnessing the potential of bioinformatics in many areas.

Evolutionary Computation in Bioinformatics

Evolutionary Computation in Bioinformatics
Author: Gary B. Fogel,David W. Corne
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.

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.

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.

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.

Evolutionary Computation Machine Learning and Data Mining in Bioinformatics

Evolutionary Computation  Machine Learning and Data Mining in Bioinformatics
Author: Elena Marchiori
Publsiher: Springer Science & Business Media
Total Pages: 302
Release: 2007-04-02
ISBN 10: 354071782X
ISBN 13: 9783540717829
Language: EN, FR, DE, ES & NL

Evolutionary Computation Machine Learning and Data Mining in Bioinformatics Book Review:

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 biomarker discovery, cell simulation and modeling, ecological modeling, fluxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, as well as systems biology

Analysis of Biological Data

Analysis of Biological Data
Author: Anonim
Publsiher: Unknown
Total Pages: 135
Release: 2022
ISBN 10: 9814475122
ISBN 13: 9789814475129
Language: EN, FR, DE, ES & NL

Analysis of Biological Data Book Review:

Bioinformatics and RNA

Bioinformatics and RNA
Author: Dolly Sharma,Shailendra Singh,Mamta Mittal
Publsiher: CRC Press
Total Pages: 148
Release: 2021-08-18
ISBN 10: 1000428338
ISBN 13: 9781000428339
Language: EN, FR, DE, ES & NL

Bioinformatics and RNA Book Review:

This book offers a unique balance between a basic introductory knowledge of bioinformatics and a detailed study of algorithmic techniques. Bioinformatics and RNA: A Practice-Based Approach is a complete guide on the fundamental concepts, applications, algorithms, protocols, new trends, challenges, and research results in the area of bioinformatics and RNA. The book offers a broad introduction to the explosively growing new discipline of bioinformatics. It covers theoretical topics along with computational algorithms. It explores RNA bioinformatics, which contribute to therapeutics and drug discovery. Implementation of algorithms in a DotNet Framework with code and complete insight on the state-of-the-art and recent advancements are presented in detail. The book targets both novice readers as well as practitioners in the field. FEATURES Offers a broad introduction to the explosively growing new discipline of bioinformatics Covers theoretical topics and computational algorithms Explores RNA bioinformatics to unleash the potential from therapeutics to drug discovery Discusses implementation of algorithms in DotNet Frameworks with code Presents insights into the state of the art and recent advancements in bioinformatics The book is useful to undergraduate students with engineering, science, mathematics, or biology backgrounds. Researchers will be equally interested.

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.

Bioinformatics

Bioinformatics
Author: Pierre Baldi,Søren Brunak
Publsiher: MIT Press (MA)
Total Pages: 351
Release: 1998
ISBN 10: 9780262024426
ISBN 13: 026202442X
Language: EN, FR, DE, ES & NL

Bioinformatics Book Review:

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.

Machine Learning and IoT

Machine Learning and IoT
Author: Shampa Sen,Leonid Datta,Sayak Mitra
Publsiher: CRC Press
Total Pages: 354
Release: 2018-07-04
ISBN 10: 1351029924
ISBN 13: 9781351029926
Language: EN, FR, DE, ES & NL

Machine Learning and IoT Book Review:

This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving complex biological problems and enhancing treatment of various grave diseases.

Artificial Intelligence and Machine Learning in Healthcare

Artificial Intelligence and Machine Learning in Healthcare
Author: Ankur Saxena,Shivani Chandra
Publsiher: Springer Nature
Total Pages: 228
Release: 2021-05-06
ISBN 10: 9811608113
ISBN 13: 9789811608117
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Machine Learning in Healthcare Book Review:

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 with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.