Rule sets mined in - AIRItaxa: Automatic Interesting Rule Mining of Taxa in Complex Communities

This repository makes available the rule sets obtained from association rule mining in the paper "AIRItaxa: Automatic Interesting Rule Mining of Taxa in complex communities", which is currently undergoing peer-review. Files description: mosj_full_rules_set_as_data_frame.rds This file includes the database of all rules mined from the MOSJ dataset, as a data frame object; mosj_full_rules_set.rds This file includes the database of all rules mined from the MOSJ dataset, as a transactions object; emose_rules_df.rds This file includes the database of all rules mined from the EMOSE dataset, as a data frame object; emose_rules.rds This file includes the database of all rules mined from the EMOSE dataset, as a transactions object. Association rule mining setting and implementation We used the arules R package (Hahsler et al., 2005) implementation of the apriori algorithm (Agrawal et al., 1993) to run association rule mining, using the EMOSE (Pascoal et al., 2023) and MOSJ (Pascoal et al., 2025) datasets. For detailed description of settings, please see the Experimental Design section of the paper: Pascoal F., Costa R., Baptista, S.M., Magalhães C., Branco P., AIRItaxa: Automatic Interesting Rule Mining of Taxa in complex communities. Under peer-review. If you use these rule sets, please cite: Pascoal F., Costa R., Baptista, S.M., Magalhães C., Branco P., AIRItaxa: Automatic Interesting Rule Mining of Taxa in complex communities. Under peer-review. Additional citation for data used for mining rules If you use the rule sets available in this repository, please cite the original papers describing and presenting the datasets: MOSJ: Pascoal, F., Branco, P., Torgo, L. et al. Definition of the microbial rare biosphere through unsupervised machine learning. Commun Biol 8, 544 (2025). https://doi.org/10.1038/s42003-025-07912-4 EMOSE: Pascoal, F., Tomasino, M. P., Piredda, R., Quero, G. M., Torgo, L., Poulain, J., Galand, P. E., Fuhrman, J. A., Mitchell, A., Tinta, T., Turk Dermastia, T., Fernandez-Guerra, A., Vezzi, A., Logares, R., Malfatti, F., Endo, H., Dąbrowska, A. M., De Pascale, F., Sánchez, P., Henry, N., Fosso, B., Wilson, B., Toshchakov, S., Ferrant, G. K., Grigorov, I., Vieira, F. R. J., Costa, R., Pesant, S., Magalhães, C. (2023). Inter-comparison of marine microbiome sampling protocols. ISME Communications, 3(1), 84. https://doi.org/10.1038/s43705-023-00278-w References Agrawal, R., Imielinski, T. and Swami, A. (1993) “Mining Association in Large Databases,” Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD ’93, pp. 207–216. Hahsler, M., Grün, B. and Hornik, K. (2005) “arules - A Computational Environment for Mining Association Rules and Frequent Item Sets,” JSS Journal of Statistical Software, 14(15). Available at: http://www.jstatsoft.org/. Pascoal F., Costa R., Baptista, S.M., Magalhães C., Branco P., AIRItaxa: Automatic Interesting Rule Mining of Taxa in complex communities. Under peer-review. Pascoal, F., Branco, P., Torgo, L. et al. Definition of the microbial rare biosphere through unsupervised machine learning. Commun Biol 8, 544 (2025). https://doi.org/10.1038/s42003-025-07912-4 Pascoal, F., Tomasino, M. P., Piredda, R., Quero, G. M., Torgo, L., Poulain, J., Galand, P. E., Fuhrman, J. A., Mitchell, A., Tinta, T., Turk Dermastia, T., Fernandez-Guerra, A., Vezzi, A., Logares, R., Malfatti, F., Endo, H., Dąbrowska, A. M., De Pascale, F., Sánchez, P., Henry, N., Fosso, B., Wilson, B., Toshchakov, S., Ferrant, G. K., Grigorov, I., Vieira, F. R. J., Costa, R., Pesant, S., Magalhães, C. (2023). Inter-comparison of marine microbiome sampling protocols. ISME Communications, 3(1), 84. https://doi.org/10.1038/s43705-023-00278-w