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Solanum lycopersicum - Fusarium oxysporum Fo47 interaction study using ml classifiers in transcriptomic data
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Advisor(s)
Abstract(s)
Fusarium oxysporum Fo47 is a pervasive endophyte that can colonize plant roots, initiating an interaction that can provide phytosanitary defenses. The response triggered by this non-pathogenic fungus is not well understood. To elucidate the Solanum lycopersicum - Fusarium oxysporum Fo47 interaction, machine learning methods were used to identify the informative genes (IGs) using publicly available transcriptomic data. The assembled dataset revealed 244 significantly differentially expressed genes (DEGs). The experimental work with machine learning classifiers achieved significant identification of these DEGs. Multilayer Perceptron (MLP) classifiers and Kernel Logistic Regression metalearners (meta-KLR) parameterization was optimized, achieving MLP-b and meta-KLR-b near optimal performance. Afterwards, these classifiers were used as attribute evaluators identifying two sets (A,B) of highest-rated genes, 393 (set A) by MLP-b and 317 (set B) by meta-KLR-b. Regarding the percent of significantly differentially expressed genes found by the classifiers compared to the total 244 DEGs, the set A presented 92.2%, while the set B presented 84.8%. Considering B⊂A, the IGs identified by MLP-b (set A) were used in the subsequent analysis. Among this 393 IGs, 379 were identified as Solanum lycopersicum genes, 1 as Escherichia coli protein (Hygromycin-B 4-O-kinase), 1 as Saccharomyces cerevisiae protein (galactose-responsive transcription factor GAL4) and 12 were unidentified. Then, a functional enrichment analysis of the Solanum lycopersicum IGs showed 283 biological processes and 20 biological pathways involved in the Solanum lycopersicum - Fo47 interaction.
Description
Keywords
Machine learning Microarray Informative genes Solanum lycopersicum
Pedagogical Context
Citation
Rodrigues, Vânia; Deusdado, Sérgio (2022). Solanum lycopersicum - Fusarium oxysporum Fo47 interaction study using ml classifiers in transcriptomic data. In Pereira, Ana I.; Andrej, Košir; Fernandes, Florbela P.; Pacheco, Maria F.; Teixeira, João Paulo; Lopes, Rui Pedro (Eds.) Optimization, Learning Algorithms and Applications: Second International Conference, OL2A 2022. Cham: Springer Nature. p. 405-418. ISBN 978-3-031-23235-0
Publisher
Springer Nature