A machine learning strategy for protein analysis

2002 ◽  
Vol 17 (2) ◽  
pp. 28-35 ◽  
Author(s):  
P. Baldi ◽  
G. Pollastri
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Protein Analysis

Machine learning strategy for predicting flutter performance of streamlined box girders

2021 ◽  
Vol 209 ◽  
pp. 104493
Author(s):  
Haili Liao ◽  
Hanyu Mei ◽  
Gang Hu ◽  
Bo Wu ◽  
Qi Wang
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Box Girders

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: the Evaluation of Text Classification Algorithms Using Machine Learning

2019 ◽  
Author(s):  
Francesc López Seguí ◽  
Ricardo Ander Egg Aguilar ◽  
Gabriel de Maeztu ◽  
Anna García-Altés ◽  
Francesc García Cuyàs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Text Classification ◽  
Learning Strategy ◽  
Care Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Face To Face ◽  
Classification Tool ◽  
The Impact

Background: the primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: the study was intended to examine the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: 20 machine learning algorithms (based on 5 types of algorithms and 4 text representation techniques)were trained using a sample of 3,559 messages (169,102 words) corresponding to 2,268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: the best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query"(precision = 0.48). Conclusion: to the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

Variability-Aware Machine Learning Strategy for 3-D NAND Flash Memories

2020 ◽  
Vol 67 (4) ◽  
pp. 1575-1580 ◽  
Author(s):  
Kyul Ko ◽  
Jang Kyu Lee ◽  
Hyungcheol Shin
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Nand Flash ◽  
Flash Memories

A Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data

2007 ◽  
pp. 67-85 ◽  
Author(s):  
Rajmund L. Somorjai ◽  
Murray E. Alexander ◽  
Richard Baumgartner ◽  
Stephanie Booth ◽  
Christopher Bowman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Data Driven ◽  
Biomedical Data ◽  
Flexible Machine
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