尼文·纳雷因:当我见到卡尔时,我们原则上同意,肯定有办法建立一个更高效的医疗系统,它能够以非常精确的方式,将病人与正确的药物进行匹配。卡尔支持我们将这个理念引向深入。我们不是利用筛选过的化学制品治疗病人,而是从人体的细胞样本入手去了解人体生物学,然后据此研发药物的。我们使用的是人工智能,而不是各种假设。
How exactly does artificial intelligence come into play here?
人工智能究竟在这个过程中起了什么样的作用?
NN: When you start with a hypothesis, you are dismissing a lot of other areas that might actually have an impact on whatever you are trying to figure out. How many times do we see drugs get to late stage trials and fail because the early science either wasn't robust enough or focused on the wrong target?
尼文·纳雷因:如果你从一个假设入手,你就排除了很多其他可能产生真正效果的领域。有多少次药物在晚期测试的失败,是因为它的早期科研不够扎实,或是选择了错误的靶标?
At Berg, we use AI to create over 14 trillion data points on only one tissue sample. It is actually humanly impossible to go through all this data and use the traditional hypothesis inference model to glean any value out of all of it. So early on when we built what we call an interrogative biology platform using AI to go through all that data. AI is actually able to take all the information from the patient's biology, clinical samples, and demographics and really categorize which ones are similar and which ones are different and then stratify those in a way that helps us understand the difference between the healthy and diseased.
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2020-09-15
2020-09-15
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