Tomic Adriana, Tomic Ivan, Waldron Levi, Geistlinger Ludwig, Kuhn Max, Spreng Rachel L, Dahora Lindsay C, Seaton Kelly E, Tomaras Georgia, Hill Jennifer, Duggal Niharika A, Pollock Ross D, Lazarus Norman R, Harridge Stephen D R, Lord Janet M, Khatri Purvesh, Pollard Andrew J, Davis Mark M
Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK.
Institute of Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA.
Patterns (N Y). 2021 Jan 8;2(1):100178. doi: 10.1016/j.patter.2020.100178.
Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of biological datasets, but the necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software, SIMON, to facilitate the application of 180+ state-of-the-art machine-learning algorithms to high-dimensional biomedical data. With an easy-to-use graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.
随着生物数据集的日益复杂,数据分析和知识发现在生物学和医学领域变得越来越重要,但所需的复杂编程技能和对算法的深入理解给大多数生物学家和临床医生开展此类研究带来了障碍。我们开发了一款模块化开源软件SIMON,以促进180多种先进机器学习算法在高维生物医学数据中的应用。凭借易于使用的图形用户界面、标准化流程以及机器学习和其他统计分析方法的自动化方法,SIMON有助于识别最优算法,并为非技术和技术研究人员提供一种资源,使他们能够识别生物医学数据中的关键模式。