Supporting Knowledge Discovery in Life Sciences

大量的公开数据是人工智能在生命科学中发挥关键作用的绝佳机会。自动化方法已被证明在支持生命科学研究方面是有效的，但挖掘复杂和非结构化数据仍然是一个主要挑战。在这种背景下，我的研究计划的目标是通过简化现有知识的获取，并支持其探索，为生命科学的知识发现做出贡献。我建议通过创建联合检索和挖掘文本和非文本数据的算法来实现这一目标。寻找现有知识的生命科学家面临着严峻的挑战，比如在文档中发现实体，检索与特定主题相关的文档和数据，或者根据他们对实验的贡献来分析数据。

The massive amount of publicly available data is an amazing opportunity for artificial intelligence to play a key role in life sciences. Automatic approaches have proven to be effective in supporting life sciences research, yet mining complex and unstructured data is still a major challenge. In this context, the objective of my research program is to contribute to knowledge discovery in life sciences by easing access to existing knowledge, and supporting its exploration. I propose to reach this objective by creating algorithms to jointly retrieve and mine textual and non-textual data. Life scientists looking for existing knowledge face critical challenges such as discovering entities in documents, retrieving documents and data relevant to specific topics, or analyze data according to their contribution to experiments.