A network-constrain Weibull AFT model for biomarkers discovery
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911785681944576 |
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| author | Angelini, Claudia De Canditiis, Daniela De Feis, Italia Iuliano, Antonella |
| author_facet | Angelini, Claudia De Canditiis, Daniela De Feis, Italia Iuliano, Antonella |
| contents | We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18242 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A network-constrain Weibull AFT model for biomarkers discovery Angelini, Claudia De Canditiis, Daniela De Feis, Italia Iuliano, Antonella Machine Learning Statistics Theory Methodology We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples. |
| title | A network-constrain Weibull AFT model for biomarkers discovery |
| topic | Machine Learning Statistics Theory Methodology |
| url | https://arxiv.org/abs/2402.18242 |