AI detection of druggable features from high content imaging data

成像数据对药物发现和疾病研究的价值现在正在实现。一波存储高密度和低密度生物图像数据的大型存储库现已公开可供使用。生物制药行业非常感兴趣的两个大数据资源是癌症成像联盟，Broad研究所JUMP联盟细胞摄动档案，其中包含超过10亿个细胞和14万个摄动，以及DeepCell正常组织成像图谱。这些资源可以进行数据挖掘和建模，以预测化学和遗传扰动对细胞的影响。在形态学分析中，从细胞的显微镜图像中提取定量数据，以识别生物学上相关的相似性和差异性。丰富的特征数据（包括尺寸、形状、纹理和强度的测量）产生适合检测细微表型和细胞谱系信息的轮廓。该项目将涉及从正常和受干扰细胞状态的形态学图像中提取有价值的生物标志物特征。威廉将研究这些特征与其他组学模式和表型的相关性，并将在深度学习领域发展专业知识，以解释扰动对细胞的影响。

The value of imaging data to drug discovery and disease research is now being realised. A wave of several large repositories storing high and low density bio-image data are now publicly available for use. Two such big-data resources that are of great interest to the biopharmaceutical industry are the Cancer Imaging Consortium, Broad Institute JUMP Consortium cellular perturbations archive which contains more than 1 billion cells with 140,000 perturbations and the DeepCell normal tissues imaging atlas. These resources can be data-mined and modelled to predict the effect of chemical and genetic perturbations on cells. In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences. Rich feature data (including measures of size, shape, texture and intensity) produce profiles suitable for the detection of subtle phenotypes and cell lineage information. The project will involve extracting valuable biomarker features from morphological images in normal and perturbed cellular states. William will investigate the correlation of these features with other 'omics modalities and phenotypes, and will develop expertise in the field of deep learning to interpret the effects of perturbations on cells.