Modern machine-learning tools for crystallography
نویسندگان
چکیده
منابع مشابه
Modern Applications of Machine Learning
Machine learning is one of the older areas of artificial intelligence and concerns the study of computational methods for the discovery of new knowledge and for the management of existing knowledge. Machine learning methods have been applied to various application domains. However, in the few last years due to various technological advances and research efforts (e.g. completion of the Human Gen...
متن کاملMachine learning for metagenomics: methods and tools
Owing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis. We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuse...
متن کاملMachine Learning in Modern Well Testing
Well testing is a crucial stage in the decision of setting up new wells on oil field. Decision makers rely on the metrics to evaluate the candidate wells’ potential. One important metric is permeability, measuring the ability of porous material to transmit fluids. High permeability often leads to high yielding. In a conventional well test, the well is controlled to produce at a constant flow ra...
متن کاملGraphical tools for macromolecular crystallography in PHENIX
A new Python-based graphical user interface for the PHENIX suite of crystallography software is described. This interface unifies the command-line programs and their graphical displays, simplifying the development of new interfaces and avoiding duplication of function. With careful design, graphical interfaces can be displayed automatically, instead of being manually constructed. The resulting ...
متن کاملModern Probabilistic Machine Learning and Control Methods for Portfolio Optimization
Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Acta Crystallographica Section A Foundations and Advances
سال: 2017
ISSN: 2053-2733
DOI: 10.1107/s2053273317090118