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.
Introduction to Protein Structure Prediction
Introduction to Protein Structure 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 Introduction to Protein Structure Prediction write by Huzefa Rangwala. This book was released on 2011-03-16. Introduction to Protein Structure Prediction available in PDF, EPUB and Kindle. A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
Machine Learning Meets Quantum Physics
Machine Learning Meets Quantum Physics - 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 Meets Quantum Physics write by Kristof T. Schütt. This book was released on 2020-06-03. Machine Learning Meets Quantum Physics available in PDF, EPUB and Kindle. Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications
Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications - 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 Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications write by K. G. Srinivasa. This book was released on 2020-01-30. Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications available 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.
