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| Author | : Quan Zou |
| Publisher | : Frontiers Media SA |
| Total Pages | : 485 |
| Release | : 2019-12-04 |
| Genre | : |
| ISBN | : 2889632148 |
| Author | : Quan Zou |
| Publisher | : Frontiers Media SA |
| Total Pages | : 264 |
| Release | : 2023-04-11 |
| Genre | : Science |
| ISBN | : 2889766322 |
| Author | : Sanjiban Sekhar Roy |
| Publisher | : Springer Nature |
| Total Pages | : 222 |
| Release | : 2022-06-23 |
| Genre | : Technology & Engineering |
| ISBN | : 9811691584 |
Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.
| Author | : Lukasz Kurgan |
| Publisher | : World Scientific |
| Total Pages | : 378 |
| Release | : 2022-12-06 |
| Genre | : Science |
| ISBN | : 9811258597 |
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 | : Osval Antonio Montesinos López |
| Publisher | : Springer Nature |
| Total Pages | : 707 |
| Release | : 2022-02-14 |
| Genre | : Technology & Engineering |
| ISBN | : 3030890104 |
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
| Author | : Sushmita Mitra |
| Publisher | : CRC Press |
| Total Pages | : 386 |
| Release | : 2008-06-05 |
| Genre | : Mathematics |
| ISBN | : 1420011782 |
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 |
| Publisher | : John Wiley & Sons |
| Total Pages | : 544 |
| Release | : 2021-01-20 |
| Genre | : Computers |
| ISBN | : 1119785618 |
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 | : K. G. Srinivasa |
| Publisher | : Springer Nature |
| Total Pages | : 318 |
| Release | : 2020-01-30 |
| Genre | : Technology & Engineering |
| ISBN | : 9811524459 |
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 | : Paolo Frasconi |
| Publisher | : |
| Total Pages | : 264 |
| Release | : 2003 |
| Genre | : Computers |
| ISBN | : |
The 14 papers consider how various methods in artificial intelligence are applied to problems in bioinformatics. Among the topics are statistical learning and kernel methods in bioinformatics, new machine learning methods for predicting protein topologies, multiple sequence alignments information in structure and function prediction, pattern discovery and the algorithms of surprise, the computational identification of regulatory sites in DNA sequences, computer system gene discovery for promoter structure analysis, and data acquisition and analysis in near-genome-wide expressions screening of tumor suppressor pathways using model cell lines with inducible transcription factors. There is no subject index. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com).
| Author | : Ting Hu |
| Publisher | : Frontiers Media SA |
| Total Pages | : 74 |
| Release | : 2020-12-15 |
| Genre | : Science |
| ISBN | : 2889662292 |
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
