Morgridge Institute for Research, Madison, WI 53715, USA.
Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.
Bioinformatics. 2022 Jun 24;38(Suppl 1):i10-i18. doi: 10.1093/bioinformatics/btac233.
The increasing prevalence and importance of machine learning in biological research have created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible or inappropriate for biologists because they require significant computational and mathematical knowledge, demand an unrealistic time-investment or teach skills primarily for computational researchers. We created the Machine Learning for Biologists (ML4Bio) workshop, a short, intensive workshop that empowers biological researchers to comprehend machine learning applications and pursue machine learning collaborations in their own research. The ML4Bio workshop focuses on classification and was designed around three principles: (i) emphasizing preparedness over fluency or expertise, (ii) necessitating minimal coding and mathematical background and (iii) requiring low time investment. It incorporates active learning methods and custom open-source software that allows participants to explore machine learning workflows. After multiple sessions to improve workshop design, we performed a study on three workshop sessions. Despite some confusion around identifying subtle methodological flaws in machine learning workflows, participants generally reported that the workshop met their goals, provided them with valuable skills and knowledge and greatly increased their beliefs that they could engage in research that uses machine learning. ML4Bio is an educational tool for biological researchers, and its creation and evaluation provide valuable insight into tailoring educational resources for active researchers in different domains.
Workshop materials are available at https://github.com/carpentries-incubator/ml4bio-workshop and the ml4bio software is available at https://github.com/gitter-lab/ml4bio.
Supplementary data are available at Bioinformatics online.
机器学习在生物研究中的日益普及和重要性，使得针对生物研究人员的机器学习培训资源成为一种需求。然而，现有的资源对于生物学家来说通常是无法访问、不可行或不适用的，因为它们需要大量的计算和数学知识，要求不切实际的时间投入，或者教授的技能主要是针对计算研究人员的。我们创建了机器学习生物学家研讨会（ML4Bio），这是一个短期、密集的研讨会，使生物研究人员能够理解机器学习应用，并在自己的研究中追求机器学习合作。ML4Bio 研讨会侧重于分类，其设计围绕三个原则：（i）强调准备而不是流利或专业，（ii）需要最小的编码和数学背景，（iii）要求低时间投入。它结合了主动学习方法和定制的开源软件，允许参与者探索机器学习工作流程。在多次会议以改进研讨会设计之后，我们对三个研讨会进行了研究。尽管在识别机器学习工作流程中的微妙方法缺陷方面存在一些混淆，但参与者普遍报告说，研讨会达到了他们的目标，为他们提供了有价值的技能和知识，并大大增加了他们对使用机器学习进行研究的信心。ML4Bio 是生物研究人员的教育工具，它的创建和评估为针对不同领域的活跃研究人员定制教育资源提供了有价值的见解。
研讨会材料可在 https://github.com/carpentries-incubator/ml4bio-workshop 获得，ml4bio 软件可在 https://github.com/gitter-lab/ml4bio 获得。
补充数据可在《生物信息学》在线获得。
