Literature-based discovery for cancer biology

在过去的几十年里，发表的科学的数量急剧增加，特别是在生物医学等快速发展的领域。PubMed(美国国家医学图书馆的文献服务)提供了超过2300万条引文，每天增加数千条记录。现在，科学家不可能阅读所有与他们所在领域相关的文献，更不用说邻近的领域了。因此，产生证据的关键假说往往在第一次发表很久之后才被发现，导致研究时间和资源的浪费。这阻碍了在解决基本问题方面取得的进展，如了解疾病的根本机制和开发有效治疗和预防这些疾病的手段。基于文献的自动发现(LBD)就是为了解决这个问题。它通过结合文学中已知的知识来产生新的知识。LBD促进了大规模假设检验和从大量文献集合中生成，可以显著支持科学研究。它被用来在文本中识别例如基因、药物和疾病之间的新联系，并导致了新的科学发现(例如识别候选基因和疾病的治疗)。然而，基于相当肤浅的技术(如词典匹配)，当前的LBD仅捕获了文献中的部分信息。Text Mining(TM)使得能够对生物医学文本进行自动分析，从而打开了更深、更广的覆盖面和动态LBD的大门，能够更好地随着科学的发展而演变。在过去的十年里，TM在生物医学中得到了大量的应用，并产生了支持重要任务的工具，如文献整理和语义数据库的开发。尽管TM可以类似地支持LBD，但这方面的工作很少。扩展自适应自然语言处理(NLP)和TM的最新发展，我们将开发改进的方法来识别不同生物医学文本中的概念、事件和关系。我们还将介绍新的、改进的知识发现方法，该方法使用链接预测在TM产生的复杂概念网络中进行高质量的LBD。链接预测可以最大限度地利用TM产生的丰富信息，提高LBD的精度，并产生对科学家更有用的输出。为了评估和展示由此产生的方法的好处，我们最初将把这种方法的目标对准癌症生物学的文献密集的跨学科领域。我们将与癌症研究人员密切合作开发LBD工具，并将通过使用该工具进行案例研究来评估该工具，以调查癌症生物学中当前的研究问题。最有希望的发现将通过实验室实验进行评估和验证。这项研究产生的所有数据、资源、结果和技术都将免费提供。我们希望我们的项目(I)提高LBD的能力，以便它能够在未来以类似于广泛使用的检索和测序工具的方式支持科学发现，(Ii)提高TM和LBD的适应性和便携性，(Iii)生产第一个用于癌症生物学的专用LBD工具，以及(Iii)为在现实生活生物医学研究中整合先进的TM和基于DM的LBD提供一个重要的案例研究。

Over the past decades, the volume of published science has increased dramatically, particularly in rapidly developing areas such as biomedicine. PubMed (the US National Library of Medicine's literature service) provides access to more than 23M citations, adding thousands of records daily. It is now impossible for scientists to read all the literature relevant to their field, let alone adjacent fields. As a consequence, critical hypothesis generating evidence is often discovered long after it was first published, leading to wasted research time and resources. This hinders the progress on solving fundamental problems such as understanding the mechanisms underlying diseases and developing the means for their effective treatment and prevention. Automated Literature Based Discovery (LBD) aims to address this problem. It generates new knowledge by combining what is already known in literature. Facilitating large-scale hypothesis testing and generation from huge collections of literature, LBD could significantly support scientific research. It has been used to identify new connections between e.g. genes, drugs and diseases in texts and it has resulted in new scientific discoveries (e.g. identification of candidate genes and treatments for illnesses). However, based on fairly shallow techniques (e.g. dictionary matching) current LBD captures only some of the information available in literature.Enabling automatic analysis of biomedical texts, Text Mining (TM) could open the doors to much deeper, wider coverage and dynamic LBD better capable of evolving with the development of science. The last decade has seen massive application of TM to biomedicine and has resulted in tools supporting important tasks such as literature curation and the development of semantic databases. Although TM could similarly support LBD, little work exists in this area. Extending recent developments in adaptive Natural Language Processing (NLP) and TM, we will develop improved methodology for identifying concepts, events and relations in diverse biomedical texts. We will also introduce novel, improved methodology for knowledge discovery which uses link prediction for high quality LBD in the complex network of concepts resulting from TM. Link prediction can optimally exploit the rich information generated by TM, can improve the accuracy of LBD and can yield output which is more useful for scientists. To evaluate and demonstrate the benefits of the resulting approach, we will initially target this methodology to the literature-intensive, interdisciplinary area of cancer biology. We will develop an LBD tool in close collaboration with cancer researchers and will evaluate the tool by using it to conduct case studies which investigate current research problems in cancer biology. The most promising findings will be evaluated and validated via laboratory experiments. All the data, resources, results and technology resulting from this research will be made freely available. We expect our project (i) to improve the capacity of LBD so that it can, in the future, support scientific discovery in a manner similar to widely employed retrieval and sequencing tools, (ii) to improve the adaptability and portability of TM and LBD, (iii) to produce the first dedicated LBD tool for cancer biology, and (iii) to provide an important case study on integration of advanced TM and DM -based LBD in real-life biomedical research.