Model organism databases are used extensively for knowledge retrieval and knowledge sharing among biologists. With the invention of genome sequencing and protein profiling technologies, large amount of molecular data provides practical insights into the molecular study of model organisms. The knowledge-intensive characteristic of model organism databases provides a reference point for the comparative study of other species. In this paper, I argue that algorithms could be used to facilitate cross-species research. I emphasize the epistemic significance of algorithms in the integration of data for cross-species research. I examine (1) how algorithms guide data integration in model organism databases; and (2) the importance of algorithms for the use of model organism database in the cross-species research. I argue that an extrapolation from the stored data to other species is possible in virtue of the fact that algorithms can facilitate two modes of data integration—viz., inter-level and cross-species integration. Lastly, I examine the implication of the data integration role of an algorithm in light of mechanistic explanation.

模式生物数据库广泛用于生物学家之间的知识检索和知识共享。随着基因组测序和蛋白质分析技术的发明，大量的分子数据为模式生物的分子研究提供了实用的见解。模式生物数据库知识密集的特点为其他物种的比较研究提供了参考点。在本文中，我认为算法可以用来促进跨物种研究。我强调算法在跨物种研究数据整合中的认知意义。我研究了（1）算法如何指导模式生物数据库中的数据集成； (2)模式生物数据库在跨物种研究中使用算法的重要性。我认为，从存储的数据到其他物种的外推是可能的，因为算法可以促进两种数据集成模式，即层间集成和跨物种集成。最后，我根据机制解释研究了算法的数据集成作用的含义。