Chemeia: A synergistic AI integrated architecture for augmenting high value dark-data.

Chemeia：一种用于增强高价值暗数据的协同人工智能集成架构。

• 批准号: 105245
• 金额: $ 38.76万
• 依托单位: BIORELATE LIMITED
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=105245
• 关键词: Chemeia; synergistic; AI; integrated; architecture

"Data curation across scientific research and development (R&D) environments is a critical process for ensuring results from in-house experiments and external publications are effectively structured & integrated for further reuse, reinterpretation & reference. It supports a number of broad downstream activities associated with big data analytics, such as compound selection in drug discovery informatics or process optimisations in advanced materials manufacturing. These high value activities attract a broad range of competitive algorithmic software solutions (e.g. Reaxsys, Ingenuity Pathway Analysis, MaterialUniverse, EBI, IBM Watson) - all geared towards maximising existing content & are often reliant on data curation to produce it.Current methods of scientific data curation are wholly insufficient: up to 85% of scientific research is potentially wasted and 90% of scientists believe there is a reproducibility crisis (Munafo _et al_, 2017) - a global issue costing billions. This is largely down to the complexity of the task requiring experts to manually assess text & data - too slow and costly for scaling. Modern Artificial Intelligence (AI) approaches are still too inaccurate; nor do they address sparse-data completion - a common problem when integrating experimental data. Here we describe Chemeia ('Chem-ee-a'), an entirely new solution to general data curation that combines two state-of-the-art AI-centric technologies for turning static, pre built, experimental databases into more complete, reliable, and continuously monitored resources for use in these high-value R&D environments.Biorelate and Intellegens are two high-technology companies specialising in applying novel AI techniques to solve well known problems in optimising data curation for R&D. Biorelate have developed Galactic AI, an innovative platform unifying data mining, natural language processing & deep learning for curating textual data. It has been used to deliver for large pharmaceutical and biotech companies focusing on early stage drug discovery. Intellegens have developed Alchemite, which builds on existing numerical curated resources (sparse data) by using AI to predict ranked unknown data-points and uncertainties. No other solution on the market can replicate Alchemite's low cost modelling, prediction, error detection & parameter optimisation for big, sparse, numerical data.Earlier this year, both companies independently improved the volume and quality of content in a customers database and realised that each approach was limited by its ability to validate new results & predictions. Combining these two technologies so that they function synergistically, combining strengths & solving validation issues will result in a system that is capable of generating more detailed and accurate data."

“跨科学研究和开发（R&D）环境的数据管理是确保内部实验和外部出版物的结果有效结构化和集成的关键过程，以便进一步重用、重新解释和参考。它支持与大数据分析相关的许多广泛的下游活动，例如药物发现信息学中的化合物选择或先进材料制造中的工艺优化。这些高价值的活动吸引了广泛的竞争性算法软件解决方案（例如Reaxsys， Ingenuity Pathway Analysis, MaterialUniverse， EBI, IBM Watson） -所有这些都旨在最大化现有内容，并且通常依赖于数据管理来产生它。目前的科学数据管理方法是完全不够的：高达85%的科学研究可能被浪费，90%的科学家认为存在可重复性危机（Munafo等人，2017）——这是一个耗资数十亿美元的全球性问题。这在很大程度上是由于任务的复杂性，需要专家手动评估文本和数据——速度太慢，成本太高，无法扩展。现代人工智能（AI）方法仍然太不准确；它们也没有解决稀疏数据补全问题——这是整合实验数据时的一个常见问题。Chemeia （'Chem-ee-a'）是一种全新的通用数据管理解决方案，结合了两种最先进的以人工智能为中心的技术，可将静态、预构建的实验数据库转变为更完整、可靠、持续监控的资源，用于这些高价值的研发环境。Biorelate和Intellegens是两家高科技公司，专门应用新颖的人工智能技术来解决优化研发数据管理的众所周知的问题。Biorelate开发了Galactic AI，这是一个统一数据挖掘、自然语言处理和深度学习的创新平台，用于管理文本数据。它已被用于为专注于早期药物发现的大型制药和生物技术公司提供服务。Intellegens开发了Alchemite，它基于现有的数字策划资源（稀疏数据），通过使用AI来预测未知数据点和不确定性的排名。市场上没有其他解决方案可以复制Alchemite的低成本建模、预测、错误检测和参数优化，用于大型、稀疏的数值数据。今年早些时候，两家公司都独立提高了客户数据库内容的数量和质量，并意识到每种方法都受到验证新结果和预测能力的限制。将这两种技术结合起来，使它们协同工作，结合优势并解决验证问题，将使系统能够生成更详细、更准确的数据。”