• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Machine Learning and Applications >Machine Learning to Predict Developmental Neurotoxicity with High-Throughput Data from 2D Bio-Engineered Tissues
【24h】

Machine Learning to Predict Developmental Neurotoxicity with High-Throughput Data from 2D Bio-Engineered Tissues

机译：机器学习可通过2D生物工程组织的高通量数据预测发育性神经毒性

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physiology. Prior work has demonstrated success employing machine learning to predict developmental neurotoxicity using gene expression data collected from human 3D tissue models exposed to various compounds. The 3D model is biologically similar to developing neural structures, but its complexity necessitates extensive expertise and effort to employ. By instead focusing solely on constructing an assay of developmental neurotoxicity, we propose that a simpler 2D tissue model may prove sufficient. We thus compare the accuracy of predictive models trained on data from a 2D tissue model with those trained on data from a 3D tissue model, and find the 2D model to be substantially more accurate. Furthermore, we find the 2D model to be more robust under stringent gene set selection, whereas the 3D model suffers substantial accuracy degradation. While both approaches have advantages and disadvantages, we propose that our described 2D approach could be a valuable tool for decision makers when prioritizing neurotoxicity screening.
机译：越来越需要快速而准确的方法来测试几种化学暴露源之间的发育神经毒性。当前的方法，例如体内动物研究以及动物和人类原代细胞培养物的测定，面临着与时间，成本以及对人类生理学的适用性有关的挑战。先前的工作证明了使用机器学习预测成功的神经毒性的成功，该机器学习使用了从暴露于各种化合物的人3D组织模型中收集的基因表达数据。 3D模型在生物学上与开发神经结构相似，但是其复杂性需要广泛的专业知识和努力来运用。通过取而代之，仅专注于构建发育性神经毒性的检测方法，我们建议使用更简单的2D组织模型即可。因此，我们将根据2D组织模型中的数据训练的预测模型与根据3D组织模型中的数据训练的预测模型的准确性进行比较，并发现2D模型要实质上更准确。此外，我们发现2D模型在严格的基因集选择下更为稳健，而3D模型则遭受了严重的精度下降。虽然两种方法都有其优点和缺点，但我们建议，当对神经毒性筛查进行优先排序时，我们描述的2D方法对于决策者可能是有价值的工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号