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ISTRF: Identification of sucrose transporter using random forest
1College of Electrical and Information Engineering, Qu Zhou University, Quzhou, China.
Frontiers in Genetics
|September 29, 2022
Summary
A new model, ISTRF, accurately identifies sucrose transporters (SUTs), crucial proteins for plant growth and development. This method enhances identification accuracy using random forest and advanced feature representation.
Area of Science:
- Plant Biology
- Biochemistry
- Bioinformatics
Background:
- Sucrose transporter (SUT) proteins are vital transmembrane proteins in plants.
- They play key roles in sucrose transport, signaling, seed development, flowering, and growth.
Purpose of the Study:
- To develop an accurate computational model for identifying sucrose transporter proteins.
- To enhance the understanding of SUT functions in plant physiology.
Main Methods:
- Constructed a protein database from UniProt and PFAM.
- Utilized k-separated-bigrams-PSSM for protein sequence representation.
- Employed the Borderline-SMOTE algorithm to address data imbalance.
- Developed and trained a random forest-based model (ISTRF).
Main Results:
- k-separated-bigrams-PSSM proved to be a highly distinguishable feature.
- Borderline-SMOTE significantly improved model performance.
- The random forest classifier outperformed other models.
- ISTRF demonstrated superior general performance in identifying SUTs.
Conclusions:
- The ISTRF model offers a significant advancement in identifying sucrose transporter proteins.
- This computational approach aids in studying SUTs' roles in plant development and physiology.
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