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- Solanum lycopersicum - Fusarium oxysporum Fo47 interaction study using ml classifiers in transcriptomic dataPublication . Rodrigues, Vânia; Deusdado, Sérgio; Rodrigues, VâniaFusarium 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.
- Meta-learning approach for bacteria classification and identification of informative genes of the Bacillus megaterium: tomato roots tissue interactionPublication . Rodrigues, Vânia; Deusdado, Sérgio; Rodrigues, VâniaPlant growth-promoting rhizobacteria (PGPRs) are bacteria that colonize the plant roots. These beneficial bacteria have an influence on plant development through multiple mechanisms, such as nutrient availability, alleviating biotic and abiotic stress, and secrete phytohormones. Therefore, their inoculation constitutes a powerful tool towards sustainable agriculture and crop production. To understand plant-PGPRs interaction we present the classification of PGPR using machine learning and meta-learning classifiers namely Support Vector Machine (SVM), Kernel Logistic Regression (KLR), meta-SVM and meta-KLR to predict the presence of Bacillus megaterium inoculated in tomato root tissues using publicly available transcriptomic data. The original dataset presents 36 significantly differentially expressed genes. As the meta-KLR achieved near-optimal performance considering all the relevant metrics, this meta learner was afterwards used to identify the informative genes (IGs). The outcomes showed 157 IGs, being present all significantly differentially expressed genes previously identified. Among the IGs, 113 were identified as tomato genes, 5 as Bacillus subtilis proteins, 1 as Escherichia coli protein and 6 were unidentified. Then, a functional enrichment analysis of the tomato IGs showed 175 biological processes, 22 molecular functions and 20 KEGG pathways involved in B. megaterium–tomato interaction. Furthermore, the biological networks study of their Arabidopsis thaliana orthologous genes identified the co-expression, predicted interaction, shared protein domains and co-localization networks.
- Gene expression analysis of Solanum lycopersicum - Bacillus megaterium Interaction to identify informative genes using machine learning classifiersPublication . Rodrigues, Vânia; Deusdado, Sérgio; Rodrigues, VâniaThere has been a growing interest in identifying specific plant growth-promoting rhizobacteria that confer health, growth, and protective benefits to plant host. Understanding the mechanisms of this association as well as the differences that determine the different outcomes can be exploited to optimize beneficial interactions. To this end, we developed a classifier capable of predicting the presence of Bacillus megaterium inoculated in tomato root tissue and identify potential informative genes related to their interaction. Two machine learning models, Kernel Logistic Regression and Multilayer Perceptron were studied. From the 4 Multilayer Perceptron classifiers tested (MLP-a, MLP-b, MLP-c and MLP-d) with different parameters, MLP-a and MLP-c achieved near optimal performance considering all the relevant metrics. Then, these classifiers were used as attribute evaluators to identify two sets of informative genes (IGs). MLP-a showed 216 highest-rated attributes. Among these IGs, 173 were identified as Solanum lycopersicum genes, 37 were assigned to 5 Bacillus subtilis protein, 4 were assigned to 1 Escherichia coli protein and 2 were unidentified. On the other hand, MLP-c showed the same highest-rated attributes adding 27 new attributes. Based on the results of MLP-a and MLP-c, considering the identified tomato IGs, a functional enrichment analysis was developed showing nine and eight biological pathways, respectively. Furthermore, the same IGs were used to compose biological networks from Arabidopsis thaliana orthologous genes. The biological networks identified for the first set were co-expression, shared protein domains, predicted interaction and co-localization. The second set presented the same networks adding physical interaction.