Comparative analysis of feature representations and machine learning methods in Android family classification

2021 ◽  
Vol 184 ◽  
pp. 107639
Author(s):  
Yude Bai ◽  
Zhenchang Xing ◽  
Duoyuan Ma ◽  
Xiaohong Li ◽  
Zhiyong Feng
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Learning Methods ◽  
Feature Representations ◽  
Machine Learning Methods ◽  
Family Classification

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.

Using Machine Learning Methods to Solve Problems of Forecasting Demand for New Products in the Internet Marketplace

2020 ◽  
Vol 10 (4) ◽  
pp. 41-50
Author(s):  
A.A. Osin ◽  
A.K. Fomin ◽  
G.B. Sologub ◽  
V.I. Vinogradov
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
New Products ◽  
The Internet ◽  
Specific Task ◽  
Learning Methods ◽  
Online Store ◽  
Machine Learning Methods ◽  
And Storage ◽  
Internet Marketplace

The work is aimed at researching the possibility of using machine learning methods to build models for forecasting demand for new products in the online store Ozon. ru. Approaches to the solution that were not previously used in a specific task are proposed for consideration. Data on sales history and storage of goods at Ozon.ru are used as a sample. There is a description and analysis of the approximate loss of the Ozon.ru website, the data used, the process of building a base model, and the results obtained. It describes the metrics used to evaluate the prediction results and makes a comparative analysis between the prediction results of the built model and the results of heuristically selected values.

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