个性化医疗能够根据患者的分子遗传谱进行精确的肿瘤治疗。为了在分子肿瘤委员会中为患者制定最佳的靶向治疗计划，医生必须考虑基因和蛋白质水平的改变以及癌细胞表型的改变。机器学习可以发现隐藏的模式，提取关键信息，并从可用数据中揭示相应的见解。公开可用的数据集提供了必要的数据量。这项工作概述了各种机器学习算法的功效，这些算法最终可以作为精准肿瘤学环境中的临床决策支持。利用随机森林、决策树、XGBoost、逻辑回归、高斯朴素贝叶斯、k 最近邻和 AdaBoost 等算法，我们对乳腺癌数据集进行了两项实验。合并数据包括患者数据、分子数据和治疗数据。该调查的目的是根据基因谱预测药物治疗或治疗类型。经过机器学习算法的预处理和应用后，第一个结果是有希望的。在不仔细考虑其局限性的情况下，多种因素对临床护理环境中的应用提出了挑战。

Personalized medicine enables precise tumor treatment for a patient's molecular genetic profile. To devise optimal targeted treatment plans for patients in a molecular tumor board, physicians must consider alterations on gene- and proteins levels but also cancer cell phenotypes. Machine learning can uncover buried patterns, extract pivotal information, and unveil corresponding insights from available data. Publicly available datasets provide the amounts of data necessary. This work outlines the efficacy of various machine learning algorithms which could eventually serve as clinical decision support in a precision oncology setting. Leveraging algorithms including Random Forest, Decision tree, XGBoost, Logistic regression, Gaussian Naive Bayes, k nearest neighbor, and AdaBoost, we conducted two experiments for the breast invasive carcinoma dataset. Incorporated data includes patient-, molecular- and treatment data. The aim of the investigation was to predict medication treatment or type of treatment based on genetic profile. After preprocessing and application of ML algorithms, the first results were promising. Multiple factors challenge application in clinical care settings without carefully considering the limitations.