Machine learning and feature selection for drug response prediction in precision oncology applications.
Machine learning and feature selection for drug response prediction in precision oncology applications.
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DOI：
10.1007/s12551-018-0446-z
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发表时间：
2019-03
影响因子：
--
通讯作者：
Aittokallio T
中科院分区：
文献类型：
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作者：
Ali M;Aittokallio T
对从癌细胞系或患者肿瘤中测量的多个组学数据之间复杂相互作用的深入建模正在为确定针对个别癌症患者的定制治疗提供新的机会。有监督的机器学习算法越来越多地应用于组学概况，因为它们能够在高维数据集之间进行综合分析，以及使用通过特征选择和交叉验证确定的反应预测生物标记物的多组学小组对治疗反应进行个性化预测。然而，技术上的多变性和输入大数据的频繁遗漏需要应用专用的数据预处理管道，这往往会导致一些信息丢失和生物信号的压缩视图。我们在这里描述了用于抗癌药物反应建模和预测的最先进的机器学习方法，并展望了在预测抗癌化合物的表型反应时更好地利用高维多组学概况以及关于抗癌化合物靶向的癌症途径的知识的进一步机会。
In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input “big data” require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal. We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional multi-omics profiles along with knowledge about cancer pathways targeted by anti-cancer compounds when predicting their phenotypic responses.