Yu Hong Qing, O'Neill Sam, Kermanizadeh Ali
School of Computing and Human Sciences Research Centre, University of Derby, Derby DE22 3AW, UK.
Bioengineering (Basel). 2023 Sep 27;10(10):1134. doi: 10.3390/bioengineering10101134.
The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice (AIMS) framework. AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored to the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we have highlighted its prowess in three case studies of biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances.
机器学习与生物医学研究的融合为理解、诊断和治疗各种健康状况提供了新方法。然而，生物医学数据的复杂性，再加上开发和部署机器学习解决方案的复杂过程，常常给这些领域的研究人员带来重大挑战。我们在这项研究中的关键成就是引入了自动语义机器学习微服务（AIMS）框架。AIMS通过自动化机器学习流程的各个阶段来应对这些挑战，特别强调针对生物医学领域量身定制的机器学习服务本体。这个本体涵盖了从任务表示、服务建模、知识获取到知识推理以及建立自监督学习策略的所有内容。我们的框架经过精心设计，以优先考虑模型的可解释性、轻松整合领域知识并高效处理生物医学数据。此外，AIMS具有一个独特的功能：它通过强化学习技术利用自监督知识学习，并结合基于本体的策略记录模式。这使它能够自主生成、微调并持续适应机器学习模型，尤其是在面对新任务和数据时。我们的工作有两个突出贡献，表明生物医学领域的机器学习过程可以自动化，同时整合丰富的领域知识库，并为机器提供一种具有自我学习能力的方式，确保它们有效地处理新任务。为了展示AIMS的实际应用，我们在生物医学任务的三个案例研究中突出了它的优势。这些例子强调了我们的框架如何能够简化研究流程、提升科学探索的质量，并为显著进展奠定基础。
