We are determined to advance our fundamental understanding of 疾病s such as cancer, 呼吸系统疾病和心脏, 肾脏和代谢疾病. 因为通过了解导致疾病的原因, 澳门葡京赌博游戏希望找到新的治疗方法, 预防甚至治愈它们.

使用图表将知识转化为见解
Knowledge graphs are networks of contextualised scientific data facts and the relationship between them. 澳门葡京赌博游戏的知识图谱整合了基因组, 疾病, 药物, 临床和安全信息, helping to overcome confirmation bias and to turn data into insights. Machine learning and AI applications such as graph neural networks can then mine this data to uncover previously unknown patterns and make novel target predictions. In 2021, we selected the first two AI-generated 药物 targets into our portfolio, 从澳门葡京赌博游戏与 BenevolentAI. We share parts of our internal knowledge graph work on GitHub.  

解开澳门葡京赌博游戏基因内外的秘密
Our Centre for Genomics Research is working towards the analysis of 到2026年将达到200万个基因组. We use best-practice cloud environments to process and apply advanced data and AI tools to interpret the vast genomics data faster and more robustly than previously possible.

Beyond the genome lie the dynamic realms of the transcriptome, proteome and metabolome – largely untapped repositories of 丰富的 information that if connected could tell us more about what is driving 疾病. Multi-omics is the integration of these datasets which, 在机器学习和人工智能的帮助下, can help us predict what a 药物 molecule does in a cell with far greater certainty.

Every week, our pathologists analyse hundreds of tissue samples from our research studies. They check them for 疾病 and for biomarkers that may indicate patients most likely to respond to our medicines. It is very time consuming which is why we are training AI systems to assist pathologists in analysing samples accurately and more effortlessly. This has the potential to cut analysis time by over 30%.

澳门葡京赌博游戏的一个人工智能系统, we implemented an approach inspired by how some self-driving cars understand their environment. We trained the AI system to score tumour cells and immune cells for a biomarker, 叫PD-L1, which has potential to help inform immunotherapy-based treatment decisions for bladder cancer.

Cancer is not the only 疾病 where imaging and AI are transforming research. Recently one of our biopharmaceuticals research teams undertook an ambitious project to train deep neural networks to predict 疾病 risk and related biomarkers from retinal fundus images.  

Randomised Clinical Trials (RCTs) are currently the method of choice for pharma when it comes to assessing potential new medicines. However, published data shows they have become more expensive and complex over time.

Advances in data science can help us re-think clinical trials, enhancing current practice and finding new ways to discover and develop potential new medicines.

例如, the rapid adoption of high-quality Electronic Health Records (EHRs) represents a vast, 丰富的, and highly relevant data source that has a huge potential to improve clinical trial implementation.

Federated EHR technology is unlocking new opportunities to enhance clinical research and transform the way we do clinical trials. The technology has the potential to refine or replace many clinical trial processes including patient identification, 选择, 试验进行, 获取数据.

We are also employing AI and machine learning tools to glean more value from clinical trial data. 从历史上看, we have been proficient in using data from trials to analyse, interpret and report on the safety and efficacy of the trial 药物. But we want to maximise the value of the data we have already collected.

机器学习和人工智能也被应用 临床试验中的事件判定 to enable us to optimise the process at different stages with the intent of reducing the time overall. 

Data re-use can help us better design our 药物 development strategies and programmes. 这可以帮助澳门葡京赌博游戏设计更智能的试验, 加强科学发现, 并最终, 在未来, has the potential to help our patients receive the best treatments.

Today we are generating and have access to more data than ever before. Data and analytics have the potential to transform our business, but the true value of scientific data can only be realised if it is “FAIR” - Findable, 可访问的, 可互操作和可重用.

澳门葡京网赌游戏的R&D and IT groups are working closely together to create an industry-leading enterprise data and AI architecture. This will help us answer key business questions and enhance our ability to harness new tools and technologies, 比如人工智能和机器学习, 无论是现在还是将来.

澳门葡京赌博游戏还动员了一个数据科学家团队, bioinformaticians, data engineers and machine learning experts from across the company to ensure we are collecting, 以最佳方式组织和使用正确的数据.

Rapid developments in AI technology have brought us in to uncharted territory, and companies and regulators must work together to meet the new challenges posed. Our principles will empower us and our partners to navigate this new environment safely and effectively. By encouraging innovation and evolution while maintaining our values, they provide a long-term ethical foundation to uphold our AI governance.

在2020年, we engaged a diverse range of experts both inside and outside AstraZeneca to develop principles for ethical data and AI, 符合澳门葡京赌博游戏的道德准则和价值观. These values work for patients and employees and enable AstraZeneca to make a positive contribution to society.