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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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General Research Article

SVM-Root: Identification of Root-Associated Proteins in Plants by Employing the Support Vector Machine with Sequence-Derived Features

Author(s): Prabina Kumar Meher*, Siddhartha Hati, Tanmaya Kumar Sahu, Upendra Pradhan, Ajit Gupta and Surya Narayan Rath

Volume 19, Issue 1, 2024

Published on: 22 December, 2023

Page: [91 - 102] Pages: 12

DOI: 10.2174/1574893618666230417104543

Price: $65

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Abstract

Background: Root is a desirable trait for modern plant breeding programs, as the roots play a pivotal role in the growth and development of plants. Therefore, identification of the genes governing the root traits is an essential research component. With regard to the identification of root-associated genes/proteins, the existing wet-lab experiments are resource intensive and the gene expression studies are species-specific. Thus, we proposed a supervised learning-based computational method for the identification of root-associated proteins.

Methods: The problem was formulated as a binary classification, where the root-associated proteins and non-root-associated proteins constituted the two classes. Four different machine learning algorithms such as support vector machine (SVM), extreme gradient boosting, random forest, and adaptive boosting were employed for the classification of proteins of the two classes. Sequence-derived features such as AAC, DPC, CTD, PAAC, and ACF were used as input for the learning algorithms.

Results: The SVM achieved higher accuracy with the 250 selected features of AAC+DPC+CTD than that of other possible combinations of feature sets and learning algorithms. Specifically, SVM with the selected features achieved overall accuracies of 0.74, 0.73, and 0.73 when evaluated with single 5-fold cross-validation (5F-CV), repeated 5F-CV, and independent test set, respectively.

Conclusions: A web-enabled prediction tool SVM-Root (https://iasri-sg.icar.gov.in/svmroot/) has been developed for the computational prediction of the root-associated proteins. Being the first of its kind, the proposed model is believed to supplement the existing experimental methods and high throughput GWAS and transcriptome studies.

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