Bioinformatics Applications Based On Machine Learning
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Bioinformatics Applications Based On Machine Learning
Author | : Pablo Chamoso,Sara Rodríguez González,Mohd Saberi Mohamad,Alfonso González-Briones |
Publsiher | : MDPI |
Total Pages | : 206 |
Release | : 2021-09-01 |
Genre | : Technology & Engineering |
ISBN | : 9783036507606 |
Download Bioinformatics Applications Based On Machine Learning Book in PDF, Epub and Kindle
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.
Bioinformatics Applications Based On Machine Learning
Author | : Pablo Chamoso,Sara Rodriguez,Mohd Mohamad,Alfonso González-Briones |
Publsiher | : Unknown |
Total Pages | : 206 |
Release | : 2021 |
Genre | : Electronic Book |
ISBN | : 3036507612 |
Download Bioinformatics Applications Based On Machine Learning Book in PDF, Epub and Kindle
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.
Advanced AI Techniques and Applications in Bioinformatics
Author | : Loveleen Gaur,Arun Solanki,Samuel Fosso Wamba,Noor Zaman Jhanjhi |
Publsiher | : CRC Press |
Total Pages | : 220 |
Release | : 2021-10-17 |
Genre | : Technology & Engineering |
ISBN | : 9781000463019 |
Download Advanced AI Techniques and Applications in Bioinformatics Book in PDF, Epub and Kindle
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
Deep Learning in Bioinformatics
Author | : Habib Izadkhah |
Publsiher | : Academic Press |
Total Pages | : 382 |
Release | : 2022-01-08 |
Genre | : Science |
ISBN | : 9780128238363 |
Download Deep Learning in Bioinformatics Book in PDF, Epub and Kindle
Deep Learning in Bioinformatics: Techniques and Applications in Practice introduces the topic in an easy-to-understand way, exploring how it can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing and diagnosis, biomolecule interaction prediction, and in systems biology. The book also presents theoretical and practical successes of deep learning in bioinformatics, pointing out problems and suggesting future research directions. Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies. Introduces deep learning in an easy-to-understand way Presents how deep learning can be utilized for addressing some important problems in bioinformatics Presents the state-of-the-art algorithms in deep learning and bioinformatics Introduces deep learning libraries in bioinformatics
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 | : 318 |
Release | : 2020-01-30 |
Genre | : Technology & Engineering |
ISBN | : 9789811524455 |
Download Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications Book in PDF, Epub and Kindle
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
Author | : Yanqing Zhang,Jagath C. Rajapakse |
Publsiher | : John Wiley & Sons |
Total Pages | : 476 |
Release | : 2009-02-23 |
Genre | : Computers |
ISBN | : 9780470397411 |
Download Machine Learning in Bioinformatics Book in PDF, Epub and Kindle
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.
Artificial Intelligence Bioinformatics Development and Application of Tools for Omics and Inter Omics Studies
Author | : Angelo Facchiano,Dominik Heider,Davide Chicco |
Publsiher | : Frontiers Media SA |
Total Pages | : 175 |
Release | : 2020-06-18 |
Genre | : Electronic Book |
ISBN | : 9782889637522 |
Download Artificial Intelligence Bioinformatics Development and Application of Tools for Omics and Inter Omics Studies Book in PDF, Epub and Kindle
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 |
Genre | : Technology & Engineering |
ISBN | : 9783642194061 |
Download Kernel based Data Fusion for Machine Learning Book in PDF, Epub and Kindle
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.