{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T01:56:52Z","timestamp":1762999012870},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1115,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Although several methods exist to relate high-dimensional gene expression data to various clinical phenotypes, finding combinations of features in such input remains a challenge, particularly when fitting complex statistical models such as those used for survival studies.<\/jats:p><jats:p>Results: Our proposed method builds on existing \u2018regularization path-following\u2019 techniques to produce regression models that can extract arbitrarily complex patterns of input features (such as gene combinations) from large-scale data that relate to a known clinical outcome. Through the use of the data\u2019s structure and itemset mining techniques, we are able to avoid combinatorial complexity issues typically encountered with such methods, and our algorithm performs in similar orders of duration as single-variable versions. Applied to data from various clinical studies of cancer patient survival time, our method was able to produce a number of promising gene-interaction candidates whose tumour-related roles appear confirmed by literature.<\/jats:p><jats:p>Availability: An R implementation of the algorithm described in this article can be found at https:\/\/github.com\/david-duverle\/regularisation-path-following<\/jats:p><jats:p>Contact: \u00a0dave.duverle@aist.go.jp<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt532","type":"journal-article","created":{"date-parts":[[2013,9,14]],"date-time":"2013-09-14T00:19:01Z","timestamp":1379117941000},"page":"3053-3059","source":"Crossref","is-referenced-by-count":5,"title":["Discovering combinatorial interactions in survival data"],"prefix":"10.1093","volume":"29","author":[{"given":"David A.","family":"duVerle","sequence":"first","affiliation":[{"name":"1 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 2Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, 3Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and 4Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan"}]},{"given":"Ichiro","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"1 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 2Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, 3Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and 4Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan"}]},{"given":"Yuko","family":"Murakami-Tonami","sequence":"additional","affiliation":[{"name":"1 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 2Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, 3Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and 4Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan"},{"name":"1 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 2Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, 3Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and 4Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan"}]},{"given":"Kenji","family":"Kadomatsu","sequence":"additional","affiliation":[{"name":"1 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 2Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, 3Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and 4Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan"}]},{"given":"Koji","family":"Tsuda","sequence":"additional","affiliation":[{"name":"1 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 2Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan, 3Division of Molecular Oncology, Aichi Cancer Center, Nagoya, Japan and 4Department of Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan"}]}],"member":"286","published-online":{"date-parts":[[2013,9,13]]},"reference":[{"key":"2023012810485070600_btt532-B1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/1471-2164-6-37","article-title":"Gene expression signature of estrogen receptor \u03b1 status in breast cancer","volume":"6","author":"Abba","year":"2005","journal-title":"BMC Genomics"},{"key":"2023012810485070600_btt532-B2","article-title":"A lasso for hierarchical testing of interactions","author":"Bien","year":"2012","journal-title":"arXiv preprint arXiv,1211.1344"},{"key":"2023012810485070600_btt532-B3","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0166-218X(01)00341-9","article-title":"Pseudo-boolean optimization","volume":"123","author":"Boros","year":"2002","journal-title":"Discrete Appl. 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