Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?
Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?
复制标题
科学发现是一个组合优化问题：如何最好地浏览可能的实验的景观？
DOI：
10.1002/bies.201100144
复制
发表时间：
2012-03
期刊：
影响因子：
4
通讯作者：
Kell, Douglas B.
中科院分区：
文献类型：
--
作者：
Kell, Douglas B.
生物科学的相当数量的领域，包括基因和药物发现，生物体的生物技术改进的代谢工程，以及自然和定向进化的过程，最好是在一个“景观”方面，代表一个大的搜索空间的可能的解决方案或实验填充的数量相当少的实际解决方案，然后出现。这是什么使这些问题'硬'，但因此这些被视为组合优化问题，最好的攻击启发式方法从该领域已知的。此类景观也可能代表或包括多个目标，可以通过计算机模拟进行有效建模，并使用现代主动学习算法（例如基于达尔文进化论的算法）利用现有知识提供指导，以确定下一步要做的“最佳”实验。因此，认识和应用这些方法可以大大加强科学发现过程。这种分析与新兴的认识论非常吻合，这种认识论将科学推理、寻找解决方案和科学发现视为贝叶斯过程。
A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes.
登录
查看更多内容
DOI：
10.1042/bj20091474
发表时间：
2009-12-10
期刊：
The Biochemical journal
影响因子：
--
作者：
Attwood TK;Kell DB;McDermott P;Marsh J;Pettifer SR;Thorne D
通讯作者：
Thorne D
影响因子：
56.9
作者：
Daniel, C;Full, J;Wöste, L
通讯作者：
Wöste, L
影响因子：
64.8
作者：
Bershtein, Shimon;Segal, Michal;Tawfik, Dan S.
通讯作者：
Tawfik, Dan S.
影响因子：
--
作者：
Dobson PD;Smallbone K;Jameson D;Simeonidis E;Lanthaler K;Pir P;Lu C;Swainston N;Dunn WB;Fisher P;Hull D;Brown M;Oshota O;Stanford NJ;Kell DB;King RD;Oliver SG;Stevens RD;Mendes P
通讯作者：
Mendes P
影响因子：
14.4
作者：
BUCHANAN, BG;FEIGENBAUM, EA
通讯作者：
FEIGENBAUM, EA