|Table of Contents|

Comparative Study of Four Machine Learning Algorithms for Soybean Protein Localization Predicting(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2022年03期
Page:
337-344
Research Field:
Publishing date:

Info

Title:
Comparative Study of Four Machine Learning Algorithms for Soybean Protein Localization Predicting
Author(s):
LI Jia-nan1; 2;  GAO Xing-quan2;  LI Zhuo1;  TENG Xiao-hua1;  HUANG Bin1;  ZHANG Ji-cheng3;  TANG You1; 2
(1.Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China; 2.School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132000, China; 3.College of Electronic and Information, Northeast Agricultural University, Harbin 150030, China)
Keywords:
Support Vector Machines algorithm;  Naive Bayesian algorithm;  Decision Tree algorithm;  Random Forest algorithm;  soybean protein;  completely random missing;  sequence position prediction
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2022.03.0337
Abstract:
In order to explore an effective method for predicting the subcellular localization of soybean protein with different degrees of deletion, and improve the prediction ability of soybean protein subcellular localization, this study took 10 000 soybean protein sequence data with known subcellular localization positions as the research object, and carried out 5%, 10%, 15%, 20% and 30% sequences missing at random. Four machine learning methods, namely SVM algorithm, Naive Bayes algorithm, Random Forest algorithm and Decision Tree algorithm, were used to predict the subcellular position of the missing sequence. Correlation analysis was performed between the original position and the predicted position, and the accuracy and performance of different algorithms were compared and analyzed. The results showed that the prediction accuracy of Random Forest algorithm was the highest, the running speed of Naive Bayes algorithm was the fastest, and the running memory of Naive Bayes algorithm was the smallest. When the running time and running memory factors were not considered, and the prediction accuracy was high, the prediction effect of the random forest algorithm was better than the other three algorithms. In the same situation, if the running memory requirements are high, the Naive Bayes algorithm may be preferred. The results show the applicability of different machine learning methods under the prediction requirements of different degrees of missingness, and can be applied to the localization prediction of soybean protein data.

References:

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Last Update: 2022-06-30