Machine Learning Techniques on Gene Function Prediction Volume II - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning Techniques on Gene Function Prediction Volume II write by Quan Zou. This book was released on 2023-04-11. Machine Learning Techniques on Gene Function Prediction Volume II available in PDF, EPUB and Kindle.
Machine Learning Techniques on Gene Function Prediction
Machine Learning Techniques on Gene Function Prediction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning Techniques on Gene Function Prediction write by Quan Zou. This book was released on 2019-12-04. Machine Learning Techniques on Gene Function Prediction available in PDF, EPUB and Kindle.
Gene Prediction: Applying Ontology and Machine Learning (Volume II)
Gene Prediction: Applying Ontology and Machine Learning (Volume II) - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Gene Prediction: Applying Ontology and Machine Learning (Volume II) write by Casper Harvey. This book was released on 2023-09-26. Gene Prediction: Applying Ontology and Machine Learning (Volume II) available in PDF, EPUB and Kindle. 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.
Gene Prediction: Applying Ontology and Machine Learning (Volume III)
Gene Prediction: Applying Ontology and Machine Learning (Volume III) - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Gene Prediction: Applying Ontology and Machine Learning (Volume III) write by Casper Harvey. This book was released on 2023-09-26. Gene Prediction: Applying Ontology and Machine Learning (Volume III) available in PDF, EPUB and Kindle. 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.
Handbook of Machine Learning Applications for Genomics
Handbook of Machine Learning Applications for Genomics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Machine Learning Applications for Genomics write by Sanjiban Sekhar Roy. This book was released on 2022-06-23. Handbook of Machine Learning Applications for Genomics available in PDF, EPUB and Kindle. 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.
