Machine Learning Techniques on Gene Function Prediction Volume II
Author: Quan Zou
Publisher: Frontiers Media SA
Published: 2023-04-11
Total Pages: 264
ISBN-13: 2889766322
DOWNLOAD EBOOK →Author: Quan Zou
Publisher: Frontiers Media SA
Published: 2023-04-11
Total Pages: 264
ISBN-13: 2889766322
DOWNLOAD EBOOK →Author: Quan Zou
Publisher: Frontiers Media SA
Published: 2019-12-04
Total Pages: 485
ISBN-13: 2889632148
DOWNLOAD EBOOK →Author: Casper Harvey
Publisher: Larsen and Keller Education
Published: 2023-09-26
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOK →Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.
Author: Casper Harvey
Publisher: Larsen and Keller Education
Published: 2023-09-26
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOK →Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.
Author: Sanjiban Sekhar Roy
Publisher: Springer Nature
Published: 2022-06-23
Total Pages: 222
ISBN-13: 9811691584
DOWNLOAD EBOOK →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: Lihong Peng
Publisher: Frontiers Media SA
Published: 2023-01-02
Total Pages: 164
ISBN-13: 2832510345
DOWNLOAD EBOOK →Author: Osval Antonio Montesinos López
Publisher: Springer Nature
Published: 2022-02-14
Total Pages: 707
ISBN-13: 3030890104
DOWNLOAD EBOOK →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: Casper Harvey
Publisher: Larsen and Keller Education
Published: 2023-09-26
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOK →Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.
Author: Vinayagam Arunachalam
Publisher:
Published: 2007
Total Pages: 112
ISBN-13: 9783836421577
DOWNLOAD EBOOK →The objective of biological research is to understand the structural and the functional aspects of life. Though living organisms are diverse in almost every aspect, they are made of cells, and share the same machinery for their basic functions. The structural and functional aspect of life is traceable to genes, given that the information from the genes determine the protein composition and thereby the function of the cell. Hence, predicting the functions of individual genes is the gate way for understanding the blueprint of life. The rationale behind the ongoing genome sequencing projects is to utilize the sequence information to understand the genes and their functions. The exponential increase in the amount of sequence information enunciated the need for an automated approach to acquire knowledge about their biological function. This book introduces the general strategies used in the automated annotation of genes and protein sequences. Specifically, it describes a method utilizing the machine learning approach to predict gene function. This book is addressed to researchers involved in predicting gene function and applying machine learning algorithms to other biological problems.
Author: Yudong Cai
Publisher: Frontiers Media SA
Published: 2023-02-13
Total Pages: 194
ISBN-13: 2832514464
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