Transfer learning for genotype–phenotype prediction using deep learning models
Background For some understudied populations, genotype data is minimal for genotype-phenotype prediction. However, we can use the data of some other large populations to learn about the disease-causing SNPs and use that knowledge for the genotype-phenotype prediction of small populations. This manus...
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2022
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1344 |
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| _version_ | 1857415063152885760 |
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| author | Feng, Samuel |
| author2 | Muneeb, Muhammad Henschel, Andreas |
| author2_role | author author |
| author_facet | Feng, Samuel Muneeb, Muhammad Henschel, Andreas |
| author_role | author |
| dc.creator.none.fl_str_mv | Feng, Samuel Muneeb, Muhammad Henschel, Andreas |
| dc.date.none.fl_str_mv | 2022 2023-01-03T06:37:20Z 2023-01-03T06:37:20Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 10.1186/s12859-022-05036-8 1471-2105 https://depot.sorbonne.ae/handle/20.500.12458/1344 10.1186/s12859-022-05036-8 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | BMC Bioinformatics |
| dc.subject.none.fl_str_mv | Bioinformatics Genotype-phenotype Transfer learning Deep learning Genetics |
| dc.title.none.fl_str_mv | Transfer learning for genotype–phenotype prediction using deep learning models |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Background For some understudied populations, genotype data is minimal for genotype-phenotype prediction. However, we can use the data of some other large populations to learn about the disease-causing SNPs and use that knowledge for the genotype-phenotype prediction of small populations. This manuscript illustrated that transfer learning is applicable for genotype data and genotype-phenotype prediction. Results Using HAPGEN2 and PhenotypeSimulator, we generated eight phenotypes for 500 cases/500 controls (CEU, large population) and 100 cases/100 controls (YRI, small populations). We considered 5 (4 phenotypes) and 10 (4 phenotypes) different risk SNPs for each phenotype to evaluate the proposed method. The improved accuracy with transfer learning for eight different phenotypes was between 2 and 14.2 percent. The two-tailed p-value between the classification accuracies for all phenotypes without transfer learning and with transfer learning was 0.0306 for five risk SNPs phenotypes and 0.0478 for ten risk SNPs phenotypes. Conclusion The proposed pipeline is used to transfer knowledge for the case/control classification of the small population. In addition, we argue that this method can also be used in the realm of endangered species and personalized medicine. If the large population data is extensive compared to small population data, expect transfer learning results to improve significantly. We show that Transfer learning is capable to create powerful models for genotype-phenotype predictions in large, well-studied populations and fine-tune these models to populations were data is sparse. |
| id | sorbonner_21c0ea08ecdf9500eb76943fce4fdc07 |
| identifier_str_mv | 10.1186/s12859-022-05036-8 1471-2105 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1344 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Transfer learning for genotype–phenotype prediction using deep learning modelsFeng, SamuelMuneeb, MuhammadHenschel, AndreasBioinformaticsGenotype-phenotypeTransfer learningDeep learningGeneticsBackground For some understudied populations, genotype data is minimal for genotype-phenotype prediction. However, we can use the data of some other large populations to learn about the disease-causing SNPs and use that knowledge for the genotype-phenotype prediction of small populations. This manuscript illustrated that transfer learning is applicable for genotype data and genotype-phenotype prediction. Results Using HAPGEN2 and PhenotypeSimulator, we generated eight phenotypes for 500 cases/500 controls (CEU, large population) and 100 cases/100 controls (YRI, small populations). We considered 5 (4 phenotypes) and 10 (4 phenotypes) different risk SNPs for each phenotype to evaluate the proposed method. The improved accuracy with transfer learning for eight different phenotypes was between 2 and 14.2 percent. The two-tailed p-value between the classification accuracies for all phenotypes without transfer learning and with transfer learning was 0.0306 for five risk SNPs phenotypes and 0.0478 for ten risk SNPs phenotypes. Conclusion The proposed pipeline is used to transfer knowledge for the case/control classification of the small population. In addition, we argue that this method can also be used in the realm of endangered species and personalized medicine. If the large population data is extensive compared to small population data, expect transfer learning results to improve significantly. We show that Transfer learning is capable to create powerful models for genotype-phenotype predictions in large, well-studied populations and fine-tune these models to populations were data is sparse.2023-01-03T06:37:20Z2023-01-03T06:37:20Z2022Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf10.1186/s12859-022-05036-81471-2105https://depot.sorbonne.ae/handle/20.500.12458/134410.1186/s12859-022-05036-8enBMC Bioinformaticsoai:depot.sorbonne.ae:20.500.12458/13442023-01-05T07:17:01Z |
| spellingShingle | Transfer learning for genotype–phenotype prediction using deep learning models Feng, Samuel Bioinformatics Genotype-phenotype Transfer learning Deep learning Genetics |
| title | Transfer learning for genotype–phenotype prediction using deep learning models |
| title_full | Transfer learning for genotype–phenotype prediction using deep learning models |
| title_fullStr | Transfer learning for genotype–phenotype prediction using deep learning models |
| title_full_unstemmed | Transfer learning for genotype–phenotype prediction using deep learning models |
| title_short | Transfer learning for genotype–phenotype prediction using deep learning models |
| title_sort | Transfer learning for genotype–phenotype prediction using deep learning models |
| topic | Bioinformatics Genotype-phenotype Transfer learning Deep learning Genetics |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1344 |