Multi-classification Prediction of RNA-binding Proteins based on Machine Learning

DOI

10.2991/978-94-6463-300-9_38How to use a DOI?

Keywords

RNA-binding proteins; multi-classification prediction; machine learning

Abstract

RNA-Binding Proteins (RBPs) have a great impact on Ribose Nucleic Acid (RNA) stability, transport, translation, splicing and other functions. Predicting the function and mechanism of the action of RNA-binding proteins is of great significance for the fields of cell signaling and metabolic regulation, as well as diagnosis of disease mechanisms. Although there are already some mature methods in the field of RBPs prediction, most of these models are binary-classification models. These models only predict whether the protein to be tested is a particular RNA-binding protein or not. Therefore, this paper focuses on the analysis and processing of pdb_data_no_dups and data_seq two data sets, combined with random forest, decision tree and K neighborhood classifiers, committing to developing a group of multi-classification prediction models. The experimental results show that in the 5-fold cross-validation, the prediction accuracy of the random forest, decision tree and K neighborhood model in this paper can reach 0.73, 0.75 and 0.92 respectively.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haodong Suo
PY  - 2023
DA  - 2023/11/27
TI  - Multi-classification Prediction of RNA-binding Proteins based on Machine Learning
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 371
EP  - 382
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_38
DO  - 10.2991/978-94-6463-300-9_38
ID  - Suo2023
ER  -