Kaymaz, Ö, Alqahtani, K, Wood, HM orcid.org/0000-0003-3009-5904 et al. (1 more author) (2021) Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects. Journal of Applied Statistics, 48 (4). pp. 605-622. ISSN 0266-4763
Abstract
The purpose of this study is to highlight the application of sparse logistic regression models in dealing with prediction of tumour pathological subtypes based on lung cancer patients' genomic information. We consider sparse logistic regression models to deal with the high dimensionality and correlation between genomic regions. In a hierarchical likelihood (HL) method, it is assumed that the random effects follow a normal distribution and its variance is assumed to follow a gamma distribution. This formulation considers ridge and lasso penalties as special cases. We extend the HL penalty to include a ridge penalty (called ‘HLnet’) in a similar principle of the elastic net penalty, which is constructed from lasso penalty. The results indicate that the HL penalty creates more sparse estimates than lasso penalty with comparable prediction performance, while HLnet and elastic net penalties have the best prediction performance in real data. We illustrate the methods in a lung cancer study.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an author produced version of a paper published in Journal of Applied Statistics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Tumour, lung cancer, pathological subtype, logistic regression, sparse solution, hierarchical likelihood |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Mar 2020 19:12 |
Last Modified: | 05 Jul 2022 12:57 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/02664763.2020.1738358 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158919 |