NMRPy: a novel NMR scripting system to implement artificial intelligence and advanced applications

محفوظ في:
التفاصيل البيبلوغرافية
الحاوية / القاعدة:arXiv.org (Mar 27, 2021), p. n/a
المؤلف الرئيسي: Liu, Zao
مؤلفون آخرون: Song, Kan, Chen, Zhiwei
منشور في:
Cornell University Library, arXiv.org
الموضوعات:
الوصول للمادة أونلاين:Citation/Abstract
Full text outside of ProQuest
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

MARC

LEADER 00000nab a2200000uu 4500
001 2506965937
003 UK-CbPIL
022 |a 2331-8422 
035 |a 2506965937 
045 0 |b d20210327 
100 1 |a Liu, Zao 
245 1 |a NMRPy: a novel NMR scripting system to implement artificial intelligence and advanced applications 
260 |b Cornell University Library, arXiv.org  |c Mar 27, 2021 
513 |a Working Paper 
520 3 |a Background: Software is an important windows to offer a variety of complex instrument control and data processing for nuclear magnetic resonance (NMR) spectrometer. NMR software should allow researchers to flexibly implement various functionality according to the requirement of applications. Scripting system can offer an open environment for NMR users to write custom programs with basic libraries. Emerging technologies, especially multivariate statistical analysis and artificial intelligence, have been successfully applied to NMR applications such as metabolomics and biomacromolecules. Scripting system should support more complex NMR libraries, which will enable the emerging technologies to be easily implemented in the scripting environment. Result: Here, a novel NMR scripting system named "NMRPy" is introduced. In the scripting system, both Java based NMR methods and original CPython based libraries are supported. A module was built as a bridge to integrate the runtime environment of Java and CPython. It works as an extension in CPython environment, as well as interacts with Java part by Java Native Interface. Leveraging the bridge, Java based instrument control and data processing methods can be called as a CPython style. Compared with traditional scripting system, NMRPy is easier for NMR researchers to develop complex functionality with fast numerical computation, multivariate statistical analysis, deep learning etc. Non-uniform sampling and protein structure prediction methods based on deep learning can be conveniently integrated into NMRPy. Conclusion: NMRPy offers a user-friendly environment to implement custom functionality leveraging its powerful basic NMR and rich CPython libraries. NMR applications with emerging technologies can be easily integrated. The scripting system is free of charge and can be downloaded by visiting http://www.spinstudioj.net/nmrpy. 
653 |a Software 
653 |a Libraries 
653 |a Data processing 
653 |a Deep learning 
653 |a Applications programs 
653 |a Artificial intelligence 
653 |a Nuclear magnetic resonance--NMR 
653 |a Windows (computer programs) 
653 |a Numerical analysis 
653 |a Multivariate analysis 
653 |a Multivariate statistical analysis 
653 |a Bridge loads 
653 |a Statistical analysis 
653 |a New technology 
653 |a Control equipment 
700 1 |a Song, Kan 
700 1 |a Chen, Zhiwei 
773 0 |t arXiv.org  |g (Mar 27, 2021), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2506965937/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2103.14988