Support for New Bioinformatics Methods Development

新的生物信息学方法开发支持包括草甘膦毒性的图像分析，其中基于深度学习的图像处理方法用于区分HepaRG细胞和原代肝细胞的正常、应激和细胞死亡条件;为OHT组的证据映射开发证据标记协议;对SWIFT-Review程序中目前可用的现有标记方法进行评估;在文件标记活动中使用了机器学习方法，以探索SWIFT-Review目前使用的基于关键字的标记策略的替代方案。

New bioinformatics method development support includes, image analysis for glyphosate toxicity where deep-learning based image processing tmethods were used to discriminate between normal, stressed and cell-death conditions of HepaRG cells and primary hepatocytes; Evidence tagging protocols were develop for evidence mapping for the OHAT group; an evaluation of existing tagging methods was performed currently available in the SWIFT-Review program; Machine Learning methods were used for Document tagging activity exploring alternative to the keyword-based tagging strategy currently used in SWIFT-Review.