番茄黄化曲叶病毒
多层感知器
人工智能
机器学习
计算机科学
感知器
人工神经网络
生物
病毒
病毒学
植物病毒
作者
Nattanong Bupi,Vinoth Kumar Sangaraju,Le Thi Phan,Aamir Lal,Thuy Thi Bich Vo,Phuong T. Ho,Muhammad Amir Qureshi,Marjia Tabassum,Sukchan Lee,Balachandran Manavalan
出处
期刊:Research
[AAAS00]
日期:2023-01-01
卷期号:6
被引量:4
标识
DOI:10.34133/research.0016
摘要
Tomato yellow leaf curl virus (TYLCV) dispersed across different countries, specifically to subtropical regions, associated with more severe symptoms. Since TYLCV was first isolated in 1931, it has been a menace to tomato industrial production worldwide over the past century. Three groups were newly isolated from TYLCV-resistant tomatoes in 2022; however, their functions are unknown. The development of machine learning (ML)-based models using characterized sequences and evaluating blind predictions is one of the major challenges in interdisciplinary research. The purpose of this study was to develop an integrated computational framework for the accurate identification of symptoms (mild or severe) based on TYLCV sequences (isolated in Korea). For the development of the framework, we first extracted 11 different feature encodings and hybrid features from the training data and then explored 8 different classifiers and developed their respective prediction models by using randomized 10-fold cross-validation. Subsequently, we carried out a systematic evaluation of these 96 developed models and selected the top 90 models, whose predicted class labels were combined and considered as reduced features. On the basis of these features, a multilayer perceptron was applied and developed the final prediction model (IML-TYLCVs). We conducted blind prediction on 3 groups using IML-TYLCVs, and the results indicated that 2 groups were severe and 1 group was mild. Furthermore, we confirmed the prediction with virus-challenging experiments of tomato plant phenotypes using infectious clones from 3 groups. Plant virologists and plant breeding professionals can access the user-friendly online IML-TYLCVs web server at https://balalab-skku.org/IML-TYLCVs , which can guide them in developing new protection strategies for newly emerging viruses.
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