Rule sets mined in - AIRItaxa: Automatic Interesting Rule Mining of Taxa in Complex Communities
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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
本仓库公开了来自论文"AIRItaxa: Automatic Interesting Rule Mining of Taxa in complex communities"（目前处于同行评审阶段）中通过关联规则挖掘得到的规则集。 文件说明： mosj_full_rules_set_as_data_frame.rds 该文件以数据框（data frame）对象形式存储了从MOSJ数据集挖掘得到的全部规则数据库。 mosj_full_rules_set.rds 该文件以事务对象（transactions object）形式存储了从MOSJ数据集挖掘得到的全部规则数据库。 emose_rules_df.rds 该文件以数据框对象形式存储了从EMOSE数据集挖掘得到的全部规则数据库。 emose_rules.rds 该文件以事务对象形式存储了从EMOSE数据集挖掘得到的全部规则数据库。 关联规则挖掘设置与实现 我们采用arules R包（Hahsler等人，2005）实现的Apriori算法（Agrawal等人，1993），针对EMOSE（Pascoal等人，2023）与MOSJ（Pascoal等人，2025）数据集执行关联规则挖掘。关于参数设置的详细说明，请参阅论文"AIRItaxa: Automatic Interesting Rule Mining of Taxa in complex communities"的实验设计部分，作者为Pascoal F.、Costa R.、Baptista S.M.、Magalhães C.、Branco P.，目前处于同行评审阶段。 若使用本仓库提供的规则集，请引用以下文献： Pascoal F., Costa R., Baptista S.M., Magalhães C., Branco P. "AIRItaxa: Automatic Interesting Rule Mining of Taxa in complex communities"，待同行评审。 数据集挖掘所用数据的额外引用 若使用本仓库的规则集，请同时引用描述并提供对应数据集的原始文献： MOSJ数据集：Pascoal F., Branco P., Torgo L. 等. 通过无监督机器学习定义微生物稀有生物圈. 通讯生物学, 8, 544 (2025). https://doi.org/10.1038/s42003-025-07912-4 EMOSE数据集：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). 海洋微生物组采样方案的跨比较. ISME通讯, 3(1), 84. https://doi.org/10.1038/s43705-023-00278-w 参考文献 1. Agrawal R., Imielinski T. 与Swami A. (1993) "大型数据库中的关联规则挖掘"，收录于1993年ACM SIGMOD国际数据管理会议论文集 - SIGMOD ’93，第207–216页。 2. Hahsler M., Grün B. 与Hornik K. (2005) "arules：挖掘关联规则与频繁项集的计算环境"，《统计软件期刊》, 14(15). 可访问于: http://www.jstatsoft.org/. 3. Pascoal F., Costa R., Baptista S.M., Magalhães C., Branco P. "AIRItaxa: Automatic Interesting Rule Mining of Taxa in complex communities"，待同行评审。 4. Pascoal F., Branco P., Torgo L. 等. 通过无监督机器学习定义微生物稀有生物圈. 通讯生物学, 8, 544 (2025). https://doi.org/10.1038/s42003-025-07912-4 5. Pascoal F., Tomasino M.P. 等. 海洋微生物组采样方案的跨比较. ISME通讯, 3(1), 84 (2023). https://doi.org/10.1038/s43705-023-00278-w
