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Literature-based discovery for cancer biology

基于文献的癌症生物学发现

基本信息

批准号：
MR/M013049/1
负责人：
Anna Korhonen
金额：
$ 50.37万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FM013049%2F1
关键词：
Literature based discovery cancer biology

项目摘要

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.

在过去的几十年里，科学出版物的数量急剧增加，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2300万次引用，每天增加数千条记录。现在，科学家不可能阅读与他们的领域相关的所有文献，更不用说邻近的领域了。因此，产生证据的关键假设往往在首次发表很久之后才被发现，从而浪费了研究时间和资源。这阻碍了在解决基本问题方面取得进展，例如了解疾病的潜在机制和制定有效治疗和预防疾病的手段。基于文献的自动发现（LBD）旨在解决这个问题。它通过结合文学中已知的东西来产生新的知识。LBD可以促进大规模的假设检验和从大量的文献中生成，可以显著地支持科学研究。它已被用于确定文本中基因、药物和疾病之间的新联系，并导致新的科学发现（例如确定候选基因和疾病治疗方法）。然而，基于相当肤浅的技术（如字典匹配），目前的LBD只能捕获文献中可用的部分信息。实现生物医学文本的自动分析，文本挖掘（TM）可以打开大门，更深入，更广泛的覆盖和动态的LBD更好地随着科学的发展而发展。在过去的十年中，我们看到了TM在生物医学领域的大量应用，并产生了支持诸如文献管理和语义数据库开发等重要任务的工具。虽然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。链路预测可以最优地利用TM生成的丰富信息，提高LBD的精度，并产生对科学家更有用的输出。为了评估和证明所得到的方法的好处，我们将首先将这种方法用于文献密集的跨学科癌症生物学领域。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，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。本研究的所有数据、资源、结果和技术将免费提供。我们期望我们的项目(1)提高LBD的能力，使其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；(2)提高TM和LBD的适应性和可移植性；(3)生产第一个专门用于癌症生物学的LBD工具；(3)提供一个重要的案例研究，将先进的TM和基于DM的LBD整合到现实的生物医学研究中。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Cancer Hallmark Text Classification Using Convolutional Neural Networks

DOI：
10.17863/cam.12420
发表时间：
2016-12
期刊：
影响因子：
0
作者：
Simon Baker;A. Korhonen;Sampo Pyysalo
通讯作者：
Simon Baker;A. Korhonen;Sampo Pyysalo

A novel Atg5-shRNA mouse model enables temporal control of Autophagy in vivo.

DOI：
10.1080/15548627.2018.1458172
发表时间：
2018
期刊：
Autophagy
影响因子：
13.3
作者：
Cassidy LD;Young AR;Pérez-Mancera PA;Nimmervoll B;Jaulim A;Chen HC;McIntyre DJO;Brais R;Ricketts T;Pacey S;De La Roche M;Gilbertson RJ;Rubinsztein DC;Narita M
通讯作者：
Narita M

Temporal inhibition of autophagy reveals segmental reversal of aging with increased cancer risk

自噬的暂时抑制揭示了衰老的节段逆转与癌症风险增加