写的:
Executive Director, Data Sciences and Quantitative Biology, Discovery Sciences, R&D
基因组分析副总裁兼主管 & 生物信息学，基因研究中心，发现科学，R&D
人工智能(AI)正迅速将科幻小说变成科学事实. Self-driving cars are just one example of a previously unthinkable technology that looks to harness data science and AI to revolutionise how we get about. AI also has the potential to transform the way we discover and develop potential new medicines.
在澳门葡京网赌游戏，澳门葡京赌博游戏在R中使用数据科学和人工智能&整理、连接和分析不同的数据和信息. 这将有助于澳门葡京赌博游戏更好地了解疾病, 以更高的成功率识别药物靶点, 招募和设计更好的临床试验, 澳门葡京赌博游戏希望, 最终加快澳门葡京赌博游戏的设计方式, 开发和制造新药.
澳门葡京赌博游戏的工作重点是更好地了解疾病的基本原理, 使澳门葡京网赌游戏能够找到新的治疗方法, 防止, 改变甚至最终治愈疾病. 这, 与数据驱动型文化相结合, 有可能真正改变澳门葡京赌博游戏从事科学研究的方式吗. 以下是澳门葡京赌博游戏在日常工作中使用数据科学和人工智能的一些方法, helping in our pursuit of advancing science to create potential innovative medicines:
通过知识图谱建立对疾病的理解
If you’ve ever asked Google or Alexa a question, you will have used a knowledge graph. They are incredible libraries of information which can spot the connections between thousands of different sources to find you the answer you need.
Each year, the sheer amount of scientific information and data available to researchers grows. 在澳门葡京网赌游戏, we’re now beginning to harness these vast networks of scientific data facts to give our scientists the information they need about 基因, 蛋白质, 疾病和药物, 以及它们之间的关系——它们是如何相互作用的, 合作或相互对抗.
通过使用人工智能和机器学习来组合来自多个来源的信息, 澳门葡京赌博游戏希望 to draw better and faster conclusions than if we analysed all this data by human hand. AI also has the potential to find previously unexplored patterns not immediately obvious to the human eye which 澳门葡京赌博游戏希望 will lead to new understanding of 疾病 and the drugs we design to treat them.
澳门葡京赌博游戏的知识图谱允许研究人员提出关于基因的关键问题, 疾病, 药物和安全信息，以帮助确定和优先考虑药物目标. 和, 随着澳门葡京赌博游戏的数据和知识不断发展, so will our graphs which means every new experiment will benefit from everything learned before.
最终, we want to develop personalised knowledge graphs that bring the right information to the right scientist, at the right time so that each one can play their part in advancing our understanding.
利用大数据和人工智能推进基因组学研究
Our Centre for Genomics Research (CGR) team is working hard to analyse up to two million genome sequences by 2026. Having access to this wealth of information means 澳门葡京赌博游戏希望 to identify those variants, 基因, 可能导致疾病的途径或基因组的其他部分, 预测其进展和对治疗的反应. 所有这些, 使用知识图进行集成, 旨在帮助澳门葡京赌博游戏更好地了解疾病及其作用机制, 确定新的药物靶点，设计更好的临床试验.
通过访问成千上万的外显子组序列, our team of experts have developed bespoke analytical frameworks to study the genetic underpinnings of human disease. Insights emerging from the CGR currently include identifying candidate drug targets, 探索重新定位的机会, 利用自然遗传变异进行人类安全评估, 了解基于人口基因组学的市场机会, and performing real-time human genetic validation/invalidation of target propositions.
这 wealth of genomics data coupled with the expert application is enabling our team to focus on analysing and interpreting the data to advance science. 例如, we are building novel machine learning and deep learning-based methods to more objectively prioritise the 基因 or other parts of our genome that could potentially cause disease.
利用人工智能从每个实验中获得最大收益
CRISPR基因编辑技术在药物发现中发挥着重要作用. 澳门葡京赌博游戏可以将这项技术用于功能基因组学筛选, to sequentially delete every gene in the genome to ask what role those 基因 play in biology. 在癌症研究中, 澳门葡京赌博游戏使用CRISPR来识别哪些基因, 当删除, 导致对澳门葡京赌博游戏的抗癌药物产生耐药性或致敏.
从每个实验中获得最大收益, we are training machine learning and deep learning models to increase our confidence of the data and analyse the imaging-based outputs of CRISPR screens. 这 can increase the information available from the screens and helps us get answers more quickly.
超越对疾病的理解
The importance of data science and AI to AstraZeneca is not confined to disease understanding. 人工智能已经嵌入到澳门葡京赌博游戏的R中&D, enabling our scientists to see more from our imaging data and speeding up the design of clinical trials.
A common reason why a potential new drug fails during its development is that it causes harm to the liver. 但临床前肝毒性预测具有挑战性. To address this, we have created models that take a Bayesian approach to machine learning, i.e. 用概率方法进行推理. The models analyse data from many safety experiments to give predictions on whether a potential new medicine is likely to cause liver injury, and crucially capture the uncertainty of each estimate in a so-called posterior predictive distribution. 这 improves decision-making, helping ensure only drugs with acceptable side effects are progressed.
这 and many other exciting applications for AI mean we are learning where we can best harness these new technologies and further automate processes, freeing up more time for our people to do what they do best – pushing the boundaries of science to deliver life-changing medicines.
