Lane Center for Computational Biology and the Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Nat Chem Biol. 2011 Jun;7(6):327-30. doi: 10.1038/nchembio.576.
Due to the complexity of biological systems, cutting-edge machine-learning methods will be critical for future drug development. In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality problem in drug development.
由于生物系统的复杂性，最先进的机器学习方法对于未来的药物开发至关重要。特别是，需要机器视觉方法从成像分析中提取详细信息，以及主动学习方法来指导实验，以克服药物开发中的维度问题。
