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T-ReCS: stable selection of dynamically formed groups of features with application to prediction of clinical outcomes.

, , , , and . Pacific Symposium on Biocomputing (PSB), (2014)

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T-ReCS: stable selection of dynamically formed groups of features with application to prediction of clinical outcomes., , , , and . Pacific Symposium on Biocomputing (PSB), (2014)Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis., , , , , , , , , and 4 other author(s). Bioinform., 35 (7): 1204-1212 (2019)T-ReCS: Stable Selection of Dynamically Formed Groups of Features with Application to Prediction of Clinical Outcomes., , , , and . Pacific Symposium on Biocomputing, page 431-442. (2015)A comparison of self-supervised pretraining approaches for predicting disease risk from chest radiograph images., , and . MIDL, volume 227 of Proceedings of Machine Learning Research, page 1826-1858. PMLR, (2023)Evaluation of Causal Structure Learning Methods on Mixed Data Types., , and . CD@KDD, volume 92 of Proceedings of Machine Learning Research, page 48-65. PMLR, (2018)Integrated Theory-and Data-Driven Feature Selection in Gene Expression Data Analysis., , , and . ICDE, page 1525-1532. IEEE Computer Society, (2017)CausalMGM: an interactive web-based causal discovery tool., , , and . Nucleic Acids Res., 48 (Webserver-Issue): W597-W602 (2020)