周四发布的AI平台Hugging Face是一个用于理解DNA的新开源模型，可以在笔记本电脑上局部运行。
该模型与中国钟关康学院和意大利那不勒斯费德里科第二大学的研究人员合作开发，对基因组序列和自然语言进行了培训，使人们可以询问DNA变化对基因活动和健康的影响，并获得对话答案作为回报。
刘易斯·通斯特尔(Lewis Tunstall)是Hugging Face，称的高级研究科学家，该工具旨在帮助研究人员设计新药和基因疗法，它有可能帮助患者解释自己的基因组。
“我们已经在语言模型中解锁了一种新的能力，从根本上讲，使用自然语言甚至意味着非技术用户“实际上可以提出无法通过任何其他工具获取的DNA问题”。
Hugging Face被称为AI模型的本质上是GitHub，它提供了一个现成的AI工具库和一个主要运行开放量级和开源AI应用程序的平台，用于开发者，研究人员，企业，以及想要在不从零开始建设一切的情况下进行AI实验的人。 9月，Nvidia同意以近130亿美元收购该公司。 (Hugging Face也是今年早些时候OpenAI的型号大黑客攻击的中心).
DNA比自然语言更像是计算机代码。 不过，它使用As,Cs,Gs,Ts来编码信息，而不是0s和1s. 人类基因组包含60亿个这样的字母。 虽然人类基因组的测序结束
现在，AI模型正在帮助研究者解析一些这些谜团。 去年，Google DeepMind 发布 AlphaGenome，它预测DNA序列和基因突变的影响，但以数字格式进行。 还需要更多的计算能力。 Evo 2，一种由Arc Institute和Nvidia开发的模型，与学术伙伴一起讲DNA的语言，可以从头产生整个基因组。
Carbon-X将DNA和自然语言结合到对话界面中。 把它当成DNA的ChatGPT
团队从现有的语言模型开始，扩充了词汇，在六个字母组中读取DNA. 然后，他们继续培训3000亿块数据，或符号，将DNA序列、英文文本和将DNA与生物描述联系起来的例子结合起来。 DNA序列来自哺乳动物(包括人类)，其他脊椎动物，无脊椎动物，植物，真菌，以及原生动物。 Tunstall 称团队没有在病毒上训练模型
用户无法上传完整的人类基因组。 该模型经过培训后能够摄入大约5万个碱基对的DNA序列，因此用户只能一次查询基因组的小部分。
科学家使用Carbon-X的方法之一是帮助了解基因突变是良性还是致病，这是开发定向药物或基因疗法的关键步骤。 如果干预针对的是错误的突变，则可能完全无效，或者更糟糕的是，会造成意外伤害。
研究人员在将可识别或敏感的患者数据上传到前沿AI模型时会面临限制，因此Tunstall 称的团队有意设计了一个有140亿参数的小模型。 这将允许它在当地运行在用户的硬件上，而不是依赖基于云的模型。 没有数据发送到Hugging Face或任何其他公司，he 称。
Carbon-X的开发者警告说，他们还没有用实际的湿实验室实验来验证模型
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The AI platform Hugging Face on Thursday released a new open-source model for understanding DNA that can run locally on a laptop.
Developed in collaboration with researchers at Zhongguancun Academy in China and the University of Naples Federico II in Italy, the model is trained on genomic sequences and natural language so that people can ask questions about the effects of DNA changes on gene activity and health and get conversational answers in return.
Lewis Tunstall, a senior research scientist at Hugging Face, says the tool is meant to help researchers design new drugs and genetic therapies, and it could potentially aid patients in interpreting their own genomes.
“We've unlocked a new capability in language models, which is fundamentally about being able to chat with DNA,” he says of the new model, dubbed Carbon-X. Using natural language means even nontechnical users “can actually ask questions about DNA that wouldn't be accessible through any other tool.”
Known as essentially GitHub for AI models, Hugging Face provides a library of readymade AI tools and a platform for running primarily open-weight and open-source AI applications for developers, researchers, businesses, and people who want to experiment with AI without building everything from scratch. In September, Nvidia agreed to acquire the company for nearly $13 billion. (Hugging Face was also at the center of a major hack by OpenAI’s models earlier this year.)
DNA is more like computer code than natural language. Instead of 0s and 1s, though, it uses As, Cs, Gs, and Ts to encode information. A human genome contains 6 billion of these letters. While the human genome has been sequenced end to end, scientists still don’t understand how most of it works.
Now, AI models are helping researchers decipher some of those mysteries. Last year, Google DeepMind released AlphaGenome, which predicts the effects of DNA sequences and genetic mutations but does so in a numeric format. It also requires more compute power. Evo 2, a model developed by the Arc Institute and Nvidia, along with academic partners, speaks the language of DNA and can generate entire genomes from scratch.
Carbon-X integrates both DNA and natural language in a conversational interface. Think of it like a ChatGPT for DNA.
The team started with an existing language model and expanded its vocabulary to read DNA in groups of six letters. They then continued training on 300 billion chunks of data, or tokens, that combined DNA sequences, English text, and examples linking DNA to biological descriptions. The DNA sequences came from mammals (including humans), other vertebrates, invertebrates, plants, fungi, and protozoa. Tunstall says the team didn’t train the model on viruses because they didn’t want it to be used for potentially dangerous applications.
Users won’t be able to upload full human genomes. The model was trained to be able to ingest DNA sequences of roughly 50,000 base pairs, so users will only be able to query small parts of the genome at a time.
One of the ways scientists could use Carbon-X is to help understand whether a genetic mutation is benign or disease-causing, a key step in developing a targeted drug or genetic therapy. If an intervention targets the wrong mutation, it could be totally ineffective or, worse, cause unintended harm.
Researchers face restrictions when it comes to uploading identifiable or sensitive patient data to frontier AI models, so Tunstall says his team intentionally designed a small model with 14 billion parameters. That will allow it to run locally on the user’s hardware, rather than rely on cloud-based models. No data is sent to Hugging Face or any other company, he says.
The developers of Carbon-X caution that they haven’t validated the model yet with actual wet lab experiments, so they can’t say how accurate its predictions are.
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