Department of Electronics and Electrical Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.
School of Computer Science, University of Bristol, Bristol BS1 8UB, UK.
Int J Mol Sci. 2023 Oct 25;24(21):15570. doi: 10.3390/ijms242115570.
This paper describes a machine learning (ML) decision support system to provide a list of chemotherapeutics that individual multiple myeloma (MM) patients are sensitive/resistant to, based on their proteomic profile. The methodology used in this study involved understanding the parameter space and selecting the dominant features (proteomics data), identifying patterns of proteomic profiles and their association to the recommended treatments, and defining the decision support system of personalized treatment as a classification problem. During the data analysis, we compared several ML algorithms, such as linear regression, Random Forest, and support vector machines, to classify patients as sensitive/resistant to therapeutics. A further analysis examined data-balancing techniques that emerged due to the small cohort size. The results suggest that utilizing proteomics data is a promising approach for identifying effective treatment options for patients with MM (reaching on average an accuracy of 81%). Although this pilot study was limited by the small patient cohort (39 patients), which restricted the training and validation of the explored ML solutions to identify complex associations between proteins, it holds great promise for developing personalized anti-MM treatments using ML approaches.
这篇论文描述了一种机器学习（ML）决策支持系统，该系统可以根据多发性骨髓瘤（MM）患者的蛋白质组学特征，为每位患者提供一份对其敏感/耐药的化疗药物列表。本研究中使用的方法包括理解参数空间和选择主要特征（蛋白质组学数据），识别蛋白质组学特征模式及其与推荐治疗方法的关联，并将个性化治疗的决策支持系统定义为分类问题。在数据分析过程中，我们比较了几种 ML 算法，如线性回归、随机森林和支持向量机，以将患者分类为对治疗敏感/耐药。进一步的分析检查了由于小队列规模而出现的数据平衡技术。结果表明，利用蛋白质组学数据是一种很有前途的方法，可以为 MM 患者确定有效的治疗选择（平均准确率达到 81%）。尽管这项初步研究受到患者队列较小（39 名患者）的限制，这限制了所探索的 ML 解决方案在识别蛋白质之间复杂关联方面的训练和验证，但它为使用 ML 方法开发针对 MM 的个性化治疗方法提供了很大的希望。
