Utilising Quantum Machine Learning and quantum computing for genomic research and development

利用量子机器学习和量子计算进行基因组研究和开发

• 批准号: 10083188
• 金额: $ 15.29万
• 依托单位: QUANTUM BASE ALPHA LTD
• 依托单位国家: 英国
• 项目类别: Small Business Research Initiative
• 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=10083188
• 关键词: Utilising; Quantum; Machine; Learning; quantum

Quantum Base Alpha is working with the University of Edinburgh and the Medicines Discovery Catapult to utilise the potential of Quantum Computing (QC) together with Quantum Machine Learning (QML)to help the UK government department DHSC enhance medicine discovery. The project's focus is using advanced QML bioinformatics tools to study the genomics of pathogens.Genomics can be thought of as the fragmentation, sequencing, and reassembly of DNA to generate a full computational representation of this DNA. It is a cornerstone of modern medicine and biological science and research. Genomics is a rapidly growing market segment and is essential in future drug discovery.It has been shown statistically that DNA has many similarities to human languages and classic Machine Learning transformer models have given promising results in this field.QBA will incorporate quantum computing into these classic machine learning approaches to study the pathogens and suggest ncDNA regions for drug development. This should provide a quantum advantage in fidelity, accuracy and computational cost.Our intention is to create a quantum computational platform that supports the identification of disease-relevant functional regions within ncDNA sequences and develop applications against Disease X and future epidemics.

量子基地阿尔法正在与爱丁堡大学和药物发现弹射器合作，利用量子计算(QC)和量子机器学习(QML)的潜力来帮助英国政府部门DHSc加强药物发现。该项目的重点是使用先进的QML生物信息学工具来研究病原体的基因组学。基因组学可以被认为是DNA的片段、测序和重组，以产生这种DNA的完整计算表示。它是现代医学和生物科学与研究的基石。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。统计数据表明，DNA与人类语言有许多相似之处，经典的机器学习转换模型在这一领域取得了很好的结果。QBA将把量子计算融入到这些经典的机器学习方法中来研究病原体，并为药物开发提供建议。这应该会在保真度、准确性和计算成本方面提供量子优势。我们的意图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，并开发针对X疾病和未来流行病的应用程序。