Hybrid Quantum Transformer Architectures in Genomics

基因组学中的混合量子变压器架构

• 批准号: 10075813
• 金额: $ 6.34万
• 依托单位: ADVANCED MEDICALS LTD
• 依托单位国家: 英国
• 项目类别: Grant for R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10075813
• 关键词: Hybrid; Quantum; Transformer; Architectures; Genomics

Transformers and large language models have shown great potential in processing language-based information. Their strenth in connecting large and complex date sets makes them superior to conventional neural networks. Recently it has been shown that transformers (Google's BERT model) can also be applied to genomics datasets (DNA BERT). The majority of DNA (about 98%) is non-coding (ncDNA) and merely provides instructions on how to read the 2% of coding DNA that is first transcribed into mRNA and then translated into proteins. However, many complex medical conditions and also mental health conditions are associated with mutations within the ncDNA.The non-coding DNA (ncDNA) is statistically very similiar to human languages. This has been exploited with the DNA BERT. It has already been shown that the final transformer layer of the convential BERT model can be replaced with a variational quantum circuit to undertake classification tasks.The aim of our project is to explore various quantum transformer hybrids models and benchmark them against their classical counterparts. We will also investigate more efficient ways of encoding genomic data into the quantum state vector and may consider geometric principles, different state vector encoding and compression methods in this endeavour. The goal is to make predictions on functional regions within the ncDNA that have relevance for medical conditions.Genomics is a rapidly growing market segment and is essential in future drug discovery. Whilst ncDNA is a more challenging target for novel therapeutics, research has shown that selective molecules exist that can bind to these regions and block these regions. Our intention is to create a quantum computational platform that supports the identification of disease-relevant functional regions within ncDNA sequences.

转换器和大型语言模型在处理基于语言的信息方面显示出巨大的潜力。它们在连接大型和复杂数据集方面的优势使其优于传统的神经网络。最近有研究表明，转换器(谷歌的BERT模型)也可以应用于基因组数据集(DNA BERT)。大多数DNA(约98%)是非编码的(NcDNA)，只是提供如何阅读2%的编码DNA的指令，这些DNA首先转录成mRNA，然后翻译成蛋白质。然而，许多复杂的医学疾病和精神健康问题都与ncDNA内的突变有关。从统计上看，非编码DNA(NcDNA)与人类语言非常相似。这一点已经被DNA Bert利用了。已经证明，传统BERT模型的最终变换层可以被一个变分量子电路代替来承担分类任务。我们的项目的目的是探索各种量子变压器混合模型，并将它们与经典对应模型进行比较。我们还将研究将基因组数据编码为量子态矢量的更有效的方法，并可能考虑几何原理、不同的态矢量编码和压缩方法。其目标是对ncDNA中与医疗条件相关的功能区域进行预测。基因组学是一个快速增长的细分市场，对未来的药物开发至关重要。虽然ncdna对于新的治疗方法来说是一个更具挑战性的靶点，但研究表明，存在选择性分子可以与这些区域结合并阻止这些区域。我们的目的是创建一个量子计算平台，支持识别ncdna序列中与疾病相关的功能区。