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.
Machine Learning Techniques on Gene Function Prediction Volume II
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.
Genome Data Analysis, Protein Function and Structure Prediction by Machine Learning Techniques
Genome Data Analysis, Protein Function and Structure Prediction by Machine Learning Techniques - 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 Genome Data Analysis, Protein Function and Structure Prediction by Machine Learning Techniques write by Renzhi Cao. This book was released on 2016. Genome Data Analysis, Protein Function and Structure Prediction by Machine Learning Techniques available in PDF, EPUB and Kindle. The raw information of a typical human genome has been generated at 2001 by Human Genome Project. However, since there are a huge amount of data, it is still a big challenge for people to understand them, and extract useful structure and function information, such as the function of genes, the structure of proteins encoded by gene, and the function of proteins. Understanding these information is crucial for us to improve longevity and quality of life, and has a lot of applications, such as genomic medicine, drug design, and etc. In the meantime, machine learning techniques are growing rapidly and are good at processing large datasets, but many of them are limited for the impact on larger real world problems. In this thesis, three major contributions are described. First of all, we generate gene-gene interaction network from human genome conformation data by Hi-C technique, and the relationship of gene function and gene-gene interaction has been discovered. Second, we introduce a novel framework SMISS, which uses new source of information from gene-gene interaction network and uses a new way to integrate difference sources of information for protein function prediction. Finally, we introduce a tool called DeepQA which use machine learning technique to evaluate how well is the predicted protein structure, and a method MULTICOM for protein structure prediction. All of these protein structure and function prediction methods are available as software and web servers which are freely available to the scientific communities.
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.
Automated Gene Function Prediction
Automated 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 Automated Gene Function Prediction write by Vinayagam Arunachalam. This book was released on 2007. Automated Gene Function Prediction available in PDF, EPUB and Kindle. 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.
