Machine Learning In Molecular Sciences
Download Machine Learning In Molecular Sciences full books in PDF, epub, and Kindle. Read online free Machine Learning In Molecular Sciences ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads.
Author |
: Chen Qu |
Publisher |
: Springer Nature |
Total Pages |
: 323 |
Release |
: 2023-11-02 |
ISBN-10 |
: 9783031371967 |
ISBN-13 |
: 3031371968 |
Rating |
: 4/5 (67 Downloads) |
Synopsis Machine Learning in Molecular Sciences by : Chen Qu
Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.
Author |
: Bharath Ramsundar |
Publisher |
: O'Reilly Media |
Total Pages |
: 236 |
Release |
: 2019-04-10 |
ISBN-10 |
: 9781492039808 |
ISBN-13 |
: 1492039802 |
Rating |
: 4/5 (08 Downloads) |
Synopsis Deep Learning for the Life Sciences by : Bharath Ramsundar
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
Author |
: Lawrence Hunter |
Publisher |
: |
Total Pages |
: 484 |
Release |
: 1993 |
ISBN-10 |
: UOM:39015028911165 |
ISBN-13 |
: |
Rating |
: 4/5 (65 Downloads) |
Synopsis Artificial Intelligence and Molecular Biology by : Lawrence Hunter
These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.
Author |
: Alan Moses |
Publisher |
: CRC Press |
Total Pages |
: 281 |
Release |
: 2017-01-06 |
ISBN-10 |
: 9781482258608 |
ISBN-13 |
: 1482258609 |
Rating |
: 4/5 (08 Downloads) |
Synopsis Statistical Modeling and Machine Learning for Molecular Biology by : Alan Moses
• Assumes no background in statistics or computers • Covers most major types of molecular biological data • Covers the statistical and machine learning concepts of most practical utility (P-values, clustering, regression, regularization and classification) • Intended for graduate students beginning careers in molecular biology, systems biology, bioengineering and genetics
Author |
: Pierre Baldi |
Publisher |
: Cambridge University Press |
Total Pages |
: 387 |
Release |
: 2021-07 |
ISBN-10 |
: 9781108845359 |
ISBN-13 |
: 1108845355 |
Rating |
: 4/5 (59 Downloads) |
Synopsis Deep Learning in Science by : Pierre Baldi
Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.
Author |
: Hugh M. Cartwright |
Publisher |
: Royal Society of Chemistry |
Total Pages |
: 564 |
Release |
: 2020-07-15 |
ISBN-10 |
: 9781788017893 |
ISBN-13 |
: 1788017897 |
Rating |
: 4/5 (93 Downloads) |
Synopsis Machine Learning in Chemistry by : Hugh M. Cartwright
Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.
Author |
: Jon Paul Janet |
Publisher |
: American Chemical Society |
Total Pages |
: 189 |
Release |
: 2020-05-28 |
ISBN-10 |
: 9780841299009 |
ISBN-13 |
: 0841299005 |
Rating |
: 4/5 (09 Downloads) |
Synopsis Machine Learning in Chemistry by : Jon Paul Janet
Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important
Author |
: Sergio Decherchi |
Publisher |
: Frontiers Media SA |
Total Pages |
: 119 |
Release |
: 2021-06-08 |
ISBN-10 |
: 9782889668632 |
ISBN-13 |
: 2889668630 |
Rating |
: 4/5 (32 Downloads) |
Synopsis Molecular Dynamics and Machine Learning in Drug Discovery by : Sergio Decherchi
Dr. Sergio Decherchi and Dr. Andrea Cavalli are co-founders of BiKi Technologies s.r.l. - a company that commercializes a Molecular Dynamics-based software suite for drug discovery. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Author |
: Pavlo O. Dral |
Publisher |
: Elsevier |
Total Pages |
: 702 |
Release |
: 2022-09-16 |
ISBN-10 |
: 9780323886048 |
ISBN-13 |
: 0323886043 |
Rating |
: 4/5 (48 Downloads) |
Synopsis Quantum Chemistry in the Age of Machine Learning by : Pavlo O. Dral
Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by increasing simulation speed and accuracy and obtaining new insights. However, for nonspecialists, learning about this vast field is a formidable challenge. Quantum Chemistry in the Age of Machine Learning covers this exciting field in detail, ranging from basic concepts to comprehensive methodological details to providing detailed codes and hands-on tutorials. Such an approach helps readers get a quick overview of existing techniques and provides an opportunity to learn the intricacies and inner workings of state-of-the-art methods. The book describes the underlying concepts of machine learning and quantum chemistry, machine learning potentials and learning of other quantum chemical properties, machine learning-improved quantum chemical methods, analysis of Big Data from simulations, and materials design with machine learning. Drawing on the expertise of a team of specialist contributors, this book serves as a valuable guide for both aspiring beginners and specialists in this exciting field. - Compiles advances of machine learning in quantum chemistry across different areas into a single resource - Provides insights into the underlying concepts of machine learning techniques that are relevant to quantum chemistry - Describes, in detail, the current state-of-the-art machine learning-based methods in quantum chemistry
Author |
: Claire Nedellec |
Publisher |
: Lecture Notes in Artificial Intelligence |
Total Pages |
: 440 |
Release |
: 1998-04-08 |
ISBN-10 |
: UCSC:32106014025420 |
ISBN-13 |
: |
Rating |
: 4/5 (20 Downloads) |
Synopsis Machine Learning: ECML-98 by : Claire Nedellec
This book constitutes the refereed proceedings of the 10th European Conference on Machine Learning, ECML-98, held in Chemnitz, Germany, in April 1998. The book presents 21 revised full papers and 25 short papers reporting on work in progress together with two invited contributions; the papers were selected from a total of 100 submissions. The book is divided in sections on applications of ML, Bayesian networks, feature selection, decision trees, support vector learning, multiple models for classification, inductive logic programming, relational learning, instance-based learning, clustering, genetic algorithms, reinforcement learning and neural networks.