Transfer learning enables prediction of CYP2D6 haplotype function

This data here were used to train the models described in the manuscript "Transfer learning enables prediction of CYP2D6 haplotype function". The deep learning model described predicts metabolic function of <em>CYP2D6</em> star alleles. It uses two pretraining steps, first with simulated data, then with sequence data collected from liver microsomes, and finally using sequence data for <em>CYP2D6</em> star alleles. simulated_cyp2d6_diplotypes.tar.gz - This file contains sequence data and labels for simulated <em>CYP2D6 </em>data used in the first training step dalton_2019_cyp2d6_microsomes.txt - This file contains summary statistic data for liver microsome data used in the second pretraining step (originally from https://doi.org/10.1111/cts.12695) star_samples.vcf - This file contains sequence data for <em>CYP2D6 </em>star alleles derived from PharmVar (https://www.pharmvar.org/gene/CYP2D6) used in the final training step.本数据集用于训练发表于论文《迁移学习可实现CYP2D6单倍型（haplotype）功能预测》（Transfer learning enables prediction of CYP2D6 haplotype function）中的模型。所述深度学习模型可预测CYP2D6星号等位基因（star alleles）的代谢功能。该模型采用预训练与训练流程，具体分为三个阶段：首先使用模拟数据进行预训练，其次使用从肝微粒体采集的序列数据完成第二阶段预训练，最后使用CYP2D6星号等位基因的序列数据开展最终训练。 simulated_cyp2d6_diplotypes.tar.gz：该文件包含第一阶段训练所用的模拟CYP2D6双倍型（diplotypes）数据的序列数据与标签。 dalton_2019_cyp2d6_microsomes.txt：该文件包含第二阶段预训练所用的肝微粒体数据的汇总统计数据，原始数据来源于https://doi.org/10.1111/cts.12695。 star_samples.vcf：该文件包含最后阶段训练所用的、源自PharmVar数据库（https://www.pharmvar.org/gene/CYP2D6）的CYP2D6星号等位基因序列数据。