Talikka Marja, Bukharov Natalia, Hayes William S, Hofmann-Apitius Martin, Alexopoulos Leonidas, Peitsch Manuel C, Hoeng Julia
a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland.
b Translational Data Management Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters) , Boston , MA , USA.
Expert Opin Drug Discov. 2017 Aug;12(8):849-857. doi: 10.1080/17460441.2017.1335302. Epub 2017 Jun 6.
Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.
目前，在临床试验中，通过分子谱分析和NGS技术通常会生成数十万的数据点。如果不在明确的生物学背景下进行分析和解释，就不可能将这些数据真正转化为知识。目前，有许多公共和商业途径工具以及网络模型可促进此类分析。与此同时，能够获得的见解和知识高度依赖于这些资源的基础生物学内容。众包可用于确保用于解释丰富分子数据的工具所依据的生物学内容的准确性和透明度。涵盖领域：在本综述中，作者描述了药物发现中的众包。重点是那些成功使用众包方法来验证和扩充途径工具及生物网络模型的工作。还介绍了能够与社区一起构建生物网络的技术。专家观点：从本体论到生物网络模型机制完整性的评估，一群专家可被用于生物网络模型的整个开发过程。最终目标是通过从机制上解释患者在疾病预防、诊断和治疗结果方面的差异，来促进生物标志物发现和个性化医疗。
