Dutta Sreejata, Mudaranthakam Dinesh Pal, Li Yanming, Sardiu Mihaela E
Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
University of Kansas Cancer Center, Kansas City, USA.
bioRxiv. 2023 Oct 30:2023.10.25.564000. doi: 10.1101/2023.10.25.564000.
Omics datasets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these datasets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there remains a limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach, we introduce PerSEveML, an interactive web-based that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.
由于其高维度、大尺寸和非线性结构,组学数据集常常带来计算挑战。在存在罕见事件的情况下,分析这些数据集变得尤其艰巨。机器学习(ML)方法在分析罕见事件方面已受到关注,但将ML技术整合以理解潜在生物学的生物信息学工具仍探索有限。在我们之前开发的综合机器学习方法计算框架的基础上,我们引入了PerSEveML,这是一个基于网络的交互式工具,它利用众包智能来预测罕见事件并确定特征选择结构。PerSEveML通过评估指标提供了综合方法的全面概述,帮助用户了解各个ML方法对预测过程的贡献。此外,PerSEveML计算熵和排名分数,将输入特征直观地组织成一个由选定、未选定和波动类别组成的持久结构,这有助于研究人员揭示有关潜在生物学的有意义假设。我们已在三个具有从小规模到大规模极其罕见事件的不同生物复杂数据集上对PerSEveML进行了评估,并证明了其生成有效假设的能力。PerSEveML可在https://biostats-shinyr.kumc.edu/PerSEveML/和https://github.com/sreejatadutta/PerSEveML上获取。