ACTION on cancer

对癌症采取行动

• 批准号: EP/R022925/2
• 负责人: Ross King
• 金额: $ 78.55万
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR022925%2F2
• 关键词: ACTION; cancer

Death from cancer is typically both slow and painful, and few families have been spared its scourge. Cancer is also one of the world's greatest killers (13 million deaths and 22 million new cases per year by 2025), and it is estimated that every second person on the planet will develop cancer at some stage of their life. Over the last 30 years our knowledge about cancer has increased enormously, and now, for the first time, we understand the fundamental nature of the disease(s): malfunctioning in the way that cancer cells process information. All the cells in our bodies process information about their internal state, and communicate with their neighbours, and when this goes wrong cancer can occur.As everyone's cells are different, and there are very many different ways that this information processing can go wrong and cause cancer, it is not possible to design a single treatment for cancer, or even for a sub-type of cancer such as breast cancer. Instead what is needed are personalised treatments tailored to each patient's cancer. However, such personalised treatments are very expensive to design, and the expertise to do so is limited. In addition, it is often necessary to execute custom designed experiments to better understand what is the best treatment. Therefore the only way to make personalised cancer treatment available to everyone is through laboratoryautomation, and the use of artificial intelligence (AI).In this project we will develop ACTION, which will be a prototype AI system for the design of personalised cancer treatments. ACTION will focus on chemotherapies - design of drug cocktails. Given initial information about a cancer ACTION will extract all the relevant knowledge it can find about the cancer, both from databases and computational models of cellular information processing that scientists have developed. ACTION will rationally integrate this knowledge, and infer what extra knowledge is required to make the best decision on how to treat the cancer. ACTION will then automatically execute custom designed experiments using laboratory robotics to determine the missing information. Finally, using all the knowledge it has gathered, ACTION will decide on the best chemotherapy.We will evaluate ACTION using different types of cancer cells grown in the laboratory. This avoids the ethical complexities of working with patients, and is much cheaper and faster. If the development of ACTION is successful it will then move to testing with patient derived cancers.

死于癌症通常是缓慢而痛苦的，很少有家庭能幸免于难。癌症也是世界上最大的杀手之一（到2025年，每年有1300万人死亡，2200万新病例），据估计，地球上每两个人中就有一个会在生命的某个阶段患上癌症。在过去的30年里，我们对癌症的了解大大增加，现在，我们第一次了解了这种疾病的基本性质：癌细胞处理信息的方式出现故障。我们身体中的所有细胞都在处理有关其内部状态的信息，并与它们的邻居进行交流，当这一过程出错时，癌症就可能发生。由于每个人的细胞都是不同的，而且这种信息处理出错并导致癌症的方式有很多种，因此不可能为癌症设计一种单一的治疗方法，甚至不可能为癌症的一种亚型（如乳腺癌）设计一种单一的治疗方法。相反，我们需要的是针对每个患者癌症的个性化治疗。然而，这种个性化治疗的设计非常昂贵，并且这样做的专业知识有限。此外，通常需要执行定制设计的实验，以更好地了解什么是最佳治疗。因此，使每个人都能获得个性化癌症治疗的唯一方法是通过实验室自动化和人工智能（AI）的使用。在这个项目中，我们将开发ACTION，这将是一个用于设计个性化癌症治疗的原型AI系统。行动将侧重于化疗-药物鸡尾酒的设计。给定关于癌症的初始信息，ACTION将从科学家开发的细胞信息处理的数据库和计算模型中提取它所能找到的关于癌症的所有相关知识。ACTION将合理地整合这些知识，并推断出需要哪些额外的知识才能对如何治疗癌症做出最佳决定。ACTION将使用实验室机器人自动执行定制设计的实验，以确定缺失的信息。最后，ACTION将利用它所收集的所有知识来决定最好的化疗方案。我们将使用实验室中生长的不同类型的癌细胞来评估ACTION。这避免了与患者一起工作的道德复杂性，并且更便宜，更快。如果ACTION的开发成功，它将转移到对患者来源的癌症进行测试。

项目成果

Predicting rice phenotypes with meta and multi-target learning

• DOI: 10.1007/s10994-020-05881-9
• 发表时间: 2020-08
• 期刊: Machine Learning
• 影响因子: 7.5
• 作者: Oghenejokpeme I. Orhobor;N. Alexandrov;R. King

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

• DOI: 10.1038/s41540-021-00200-x
• 发表时间: 2021-10-20
• 期刊: NPJ systems biology and applications
• 影响因子: 4
• 作者: Wang K;Stevens R;Alachram H;Li Y;Soldatova L;King R;Ananiadou S;Schoene AM;Li M;Christopoulou F;Ambite JL;Matthew J;Garg S;Hermjakob U;Marcu D;Sheng E;Beißbarth T;Wingender E;Galstyan A;Gao X;Chambers B;Pan W;Khomtchouk BB;Evans JA;Rzhetsky A
• 通讯作者: Rzhetsky A

Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings

发现科学 - 第 26 届国际会议，DS 2023，葡萄牙波尔图，2023 年 10 月 9-11 日，会议记录

• DOI: 10.1007/978-3-031-45275-8_42
• 发表时间: 2023
• 作者: Gower A

Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction

• DOI: 10.48550/arxiv.2301.07568
• 发表时间: 2023-01
• 期刊: ArXiv
• 作者: A. Abdel-Rehim;Oghenejokpeme I. Orhobor;Hang Lou;Hao Ni;R. King

LGEM$^\text{+}$: a first-order logic framework for automated improvement of metabolic network models through abduction

LGEM$^ ext{ }$：通过溯因自动改进代谢网络模型的一阶逻辑框架

• DOI: 10.48550/arxiv.2306.06065
• 发表时间: 2023
• 作者: Gower A