Machine Learning Methods to Speed up Compositional Reservoir Simulation

2012 ◽  
Author(s):  
Vassilis Gaganis ◽  
Nikos Varotsis
Keyword(s):  
Machine Learning ◽  
Reservoir Simulation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Speed Up

A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods

2019 ◽  
Vol 20 (5) ◽  
pp. 540-550 ◽  
Author(s):  
Jiu-Xin Tan ◽  
Hao Lv ◽  
Fang Wang ◽  
Fu-Ying Dao ◽  
Wei Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Catalytic Mechanism ◽  
Biological Function ◽  
Learning Methods ◽  
Biochemical Processes ◽  
Machine Learning Methods ◽  
Enzyme Family ◽  
The Family ◽  
Speed Up ◽  
Family Classification

Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.

scholarly journals Research Progress of Gliomas in Machine Learning

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3169
Author(s):  
Ning Zhang ◽  
Yameng Wu ◽  
Yu Guo ◽  
Yu Sa ◽  
Qifeng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Technologies ◽  
Research Progress ◽  
Literature Mining ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Integrate Analysis ◽  
Speed Up ◽  
Biomedical Publications ◽  
Future Orientations

In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.

Sign in / Sign up

Export Citation Format

Share Document