Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement.
In: Genome medicine, Jg. 16 (2024-06-04), Heft 1, S. 76
Online
academicJournal
Zugriff:
Background: Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets.
Methods: We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups.
Results: Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations.
Conclusions: This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
(© 2024. The Author(s).)
| Titel: |
Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement.
|
|---|---|
| Autor/in / Beteiligte Person: | Gao, Y ; Cui, Y |
| Link: | |
| Zeitschrift: | Genome medicine, Jg. 16 (2024-06-04), Heft 1, S. 76 |
| Veröffentlichung: | [London] : BioMed Central, 2024 |
| Medientyp: | academicJournal |
| ISSN: | 1756-994X (electronic) |
| DOI: | 10.1186/s13073-024-01345-0 |
| Schlagwort: |
|
| Sonstiges: |
|