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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2504.01520 |
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| _version_ | 1866915223290511360 |
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| author | Ravi, Dayasri Groll, Andreas |
| author_facet | Ravi, Dayasri Groll, Andreas |
| contents | The integration of high-dimensional genomic data and clinical data into time-to-event prediction models has gained significant attention due to the growing availability of these datasets. Traditionally, a Cox regression model is employed, concatenating various covariate types linearly. Given that much of the data may be redundant or irrelevant, feature selection through penalization is often desirable. A notable characteristic of these datasets is their organization into blocks of distinct data types, such as methylation and clinical predictors, which requires selecting a subset of covariates from each group due to high intra-group correlations. For this reason, we propose utilizing Exclusive Lasso regularization in place of standard Lasso penalization. We apply our methodology to a real-life cancer dataset, demonstrating enhanced survival prediction performance compared to the conventional Cox regression model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time-to-event prediction for grouped variables using Exclusive Lasso Ravi, Dayasri Groll, Andreas Methodology Computation Machine Learning The integration of high-dimensional genomic data and clinical data into time-to-event prediction models has gained significant attention due to the growing availability of these datasets. Traditionally, a Cox regression model is employed, concatenating various covariate types linearly. Given that much of the data may be redundant or irrelevant, feature selection through penalization is often desirable. A notable characteristic of these datasets is their organization into blocks of distinct data types, such as methylation and clinical predictors, which requires selecting a subset of covariates from each group due to high intra-group correlations. For this reason, we propose utilizing Exclusive Lasso regularization in place of standard Lasso penalization. We apply our methodology to a real-life cancer dataset, demonstrating enhanced survival prediction performance compared to the conventional Cox regression model. |
| title | Time-to-event prediction for grouped variables using Exclusive Lasso |
| topic | Methodology Computation Machine Learning |
| url | https://arxiv.org/abs/2504.01520 |