Support Vector Machine with Fisher Score Feature Selection to Predict Disease-Resistant Gene in Rice

G. S. Saragih, Z. Rustam

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


Indonesia is one of the leading rice producers and biggest rice consumers in the world. Hence, the sustainability of rice production systems needs to be considered. Bacterial leaf blight is one of the diseases that distract the growth of rice. But, BLB can be controlled through the development of BLB resistant. The experimental method (in vitro) is one of common ways to prevent it, but this method is not efficient and having a big error. Thus, a new method called computational method required to overcome these shortcomings. There are three steps of that method in predicting genes resistance disease which are extraction, feature selection, and machine learning. The global encoding was used in the feature extraction process, then fisher score was used to select the feature. After that, a model that represents the data to analyze disease-resistant gene in rice was built. The Support Vector Machine (SVM) was applied. The result was obtained that with only ten features and the performance of purpose method, the model could represent protein information 90.91% with training data used 90%. It indicated that it was very useful. It could predict disease-resistant gene in rice with high accuracy short running time and low dimension data.

Original languageEnglish
Article number012012
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 4 Dec 2018
Event2nd Mathematics, Informatics, Science and Education International Conference, MISEIC 2018 - Surabaya, Indonesia
Duration: 21 Jul 2018 → …


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