写的:
Artificial intelligence (AI) is evolving into a powerful tool for helping scientists develop new, 癌症的创新疗法.1 虽然正在取得进展, to fully realise the potential of AI we need to maximise the use of our clinical 多组学数据. To enable this, our 数据科学 teams are optimising our data and AI foundations.
在人工智能时代改变肿瘤学
The biotech and pharmaceutical industry is in a transformative era driven by a convergence of science, 数据和技术.1 The growing accessibility of computational tools and next-generation sequencing is fostering a thriving clinical and biological data ecosystem.1 在AI的帮助下, researchers can tap into this ecosystem to speed up drug discovery, 识别疾病生物标志物并促进诊断.1
One of the most significant applications of big data and AI is in oncology.2 的承诺 精密医学 现在已成为现实, with therapies increasingly targeted to different patient populations depending on the underlying genetic makeup of their disease.2 However, 癌症是一个复杂的生物学问题, particularly tumours resistant to therapy or evolving to acquire resistance over time.3
There are vital clues for designing innovative cancer treatments hidden within the industry’s ever-growing biological and clinical data collections. 最大化这些数据的价值, 各个数据集必须统一和有组织, allowing data science and AI to drive new ideas for cancer drug 发展.
建立澳门葡京赌博游戏的肿瘤数据基础
澳门葡京赌博游戏拥有来自100多个国家的大量肿瘤学数据,000名同意的病人, 包括临床, imaging, 多组学数据. 成立于去年, the company’s 肿瘤学 数据科学 team feeds these data into a system that uses AI and other statistical tools to generate novel hypotheses in oncology drug 发展.
为了实现这种转变, 该团队正在调整澳门葡京赌博游戏复杂的数据集，使它们能够被发现, 可访问的, 可互操作的, 并且可以根据一套原则进行重用 被称为公平.4 This allows data collected from specific trials and projects to be 可访问的 across the company’s drug 发展 teams in full accordance with data protection laws worldwide.
除了澳门葡京赌博游戏临床试验的数据, we are working with external companies such as Tempus to leverage real-world data, 代表来自世界各地的病人. The strategic partnership provides crucial evidence about patient outcomes in the health system without revealing the identity of the patients in the datasets.
人工智能模型与验证:良性循环
根据FAIR原则组织数据, the 肿瘤学 数据科学 team is leveraging the latest ML techniques to construct models that guide drug 发展 efforts and the efficient design of clinical trials. 例如, the team is using knowledge graphs to integrate millions of data points to produce novel target and disease insights, and applying transformer models to identify drug response biomarkers.
A key goal for the 肿瘤学 数据科学 organisation is to decode cancer and provide actionable results to scientists and physicians at the right time so that key decisions can be made with the right data set, 从设计临床试验到选择药物靶点. Another is that bench scientists and clinicians can validate AI-based biological predictions through lab studies and trials, 生成可以反馈到模型中的数据. This creates a virtuous circle of AI-guided hypothesis generation and validation.
这个新兴领域, often termed computational oncology will stimulate innovation in our portfolio, 在癌症领域发现新的见解和证据. Our data scientists are working hard to unpick disease mechanisms, 研究肿瘤学中的新细胞通路, and deliver novel targets for our pipeline which have the potential to be addressed with our breadth of treatment platforms, 从抗体-药物结合到 t细胞衔接器.
肿瘤学数据科学的未来
数据科学 and AI are becoming increasingly important in drug discovery and 发展. 除了产生新的治疗方法, AI speeds up the work of existing drug discovery and clinical teams, helping them to make informed and highly accurate research decisions.1
在未来十年内超越这一点, we must ensure we have solid data foundations and tight circles of model validation to leverage our data resources. We’ve already seen an AI-guided approach bear fruit with our first disease models based on knowledge graphs focussed on understanding drug resistance.
We have high hopes for the progress we will make as this field continues to accelerate into the future.
