Machine learning applied to omics data
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929237850587136 |
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| author | Calviño, Aida Moreno-Ribera, Almudena Pineda, Silvia |
| author_facet | Calviño, Aida Moreno-Ribera, Almudena Pineda, Silvia |
| contents | In this chapter we illustrate the use of some Machine Learning techniques in the context of omics data. More precisely, we review and evaluate the use of Random Forest and Penalized Multinomial Logistic Regression for integrative analysis of genomics and immunomics in pancreatic cancer. Furthermore, we propose the use of association rules with predictive purposes to overcome the low predictive power of the previously mentioned models. Finally, we apply the reviewed methods to a real data set from TCGA made of 107 tumoral pancreatic samples and 117,486 germline SNPs, showing the good performance of the proposed methods to predict the immunological infiltration in pancreatic cancer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05543 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning applied to omics data Calviño, Aida Moreno-Ribera, Almudena Pineda, Silvia Genomics Machine Learning Applications In this chapter we illustrate the use of some Machine Learning techniques in the context of omics data. More precisely, we review and evaluate the use of Random Forest and Penalized Multinomial Logistic Regression for integrative analysis of genomics and immunomics in pancreatic cancer. Furthermore, we propose the use of association rules with predictive purposes to overcome the low predictive power of the previously mentioned models. Finally, we apply the reviewed methods to a real data set from TCGA made of 107 tumoral pancreatic samples and 117,486 germline SNPs, showing the good performance of the proposed methods to predict the immunological infiltration in pancreatic cancer. |
| title | Machine learning applied to omics data |
| topic | Genomics Machine Learning Applications |
| url | https://arxiv.org/abs/2402.05543 |