Current and future deep learning algorithms for MS/MS‐based small molecule structure elucidation

2021 ◽  
Author(s):  
Youzhong Liu ◽  
Thomas De Vijlder ◽  
Wout Bittremieux ◽  
Kris Laukens ◽  
Wouter Heyndrickx
Keyword(s):  
Deep Learning ◽  
Small Molecule ◽  
Structure Elucidation ◽  
Learning Algorithms ◽  
Molecule Structure

scholarly journals Enhanced measurement of residual chemical shift anisotropy for small molecule structure elucidation

2018 ◽  
Vol 54 (34) ◽  
pp. 4254-4257 ◽  
Author(s):  
Yizhou Liu ◽  
Ryan D. Cohen ◽  
Kirk R. Gustafson ◽  
Gary E. Martin ◽  
R. Thomas Williamson
Keyword(s):  
Chemical Shift ◽  
Data Quality ◽  
Structure Analysis ◽  
Small Molecule ◽  
Structure Elucidation ◽  
Chemical Shift Anisotropy ◽  
Molecular Alignment ◽  
Molecule Structure ◽  
Simple Strategy ◽  
Residual Chemical Shift Anisotropy

A simple strategy is introduced to accurately measure residual chemical shift anisotropies (RCSAs) from strong molecular alignment in the mesophase of poly-γ-(benzyl-l-glutamate). The improved data quality greatly enhances the utility of RCSA in small molecule structure analysis.

High-Throughput Oil-Encapsulated Nanodroplet Crystallisation for Organic-Soluble Small Molecule Structure Elucidation and Polymorph Screening

2019 ◽  
Author(s):  
Andrew Tyler ◽  
Ronnie Ragbirsingh ◽  
Charles James McMonagle ◽  
Paul Gordon Waddell ◽  
Sarah Elizabeth Heaps ◽  
...  
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
Structure Elucidation ◽  
Molecule Structure ◽  
Polymorph Screening

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran

2021 ◽  
Vol 35 ◽  
pp. 100825
Author(s):  
Mahdi Panahi ◽  
Khabat Khosravi ◽  
Sajjad Ahmad ◽  
Somayeh Panahi ◽  
Salim Heddam ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Infiltration Rate ◽  
Western Iran ◽  
Rate Prediction ◽  
Cumulative Infiltration
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