Towards an autonomous in silico researcher: Using logic modelling to automate the explanation of unexpected results

生物学实验经常产生意想不到的结果，即与研究人员现有的理解相矛盾的结果。这些结果可能是革命性的，揭示了新的知识或导致新的可测试的假设，但它们也可能是由于研究人员先前知识的空白或实验错误。因此，解释意外结果至关重要。然而，今天庞大的数据集包含了如此多无法解释的结果，只有少数可以手工调查，可能会让新的发现被忽视。该项目旨在开发结合实验数据、元数据和先验知识的方法，以自动解释大型数据集中所有意外数据点，加速生物学发现。

Biological experiments often produce unexpected results, i.e. results that contradict the researcher's existing understanding. Such results can be revolutionary, revealing new knowledge or leading to new testable hypotheses, but they can also be due to gaps in the researcher's prior knowledge, or experimental error. Explaining unexpected results is therefore critically important. However, today's massive datasets contain so many unexplained results only a minority can be investigated manually, potentially leaving new discoveries unnoticed. This project aims to develop methods that combine experimental data, metadata and prior knowledge to automatically explain all unexpected datapoints within a large dataset, hastening biological discoveries.