A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer
A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer
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DOI：
10.3389/fgene.2019.00256
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发表时间：
2019-03-27
影响因子：
3.7
通讯作者：
Ngom, Alioune
中科院分区：
文献类型：
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作者：
Abou Tabl, Ashraf;Alkhateeb, Abedalrhman;Ngom, Alioune
接受类似治疗的不同乳腺癌幸存者的基因组图谱可能会提供有关细胞所涉及的关键生物学过程的线索，并找到正确的治疗方法。更具体地说，这种分析可以帮助基于患者的基因表达进行个性化治疗。在本文中，我们提出了一个分层机器学习系统，可以预测接受特定治疗的患者的5年生存能力;这些类是建立在生存能力信息和给定治疗两部分的组合之上的。对于生存率信息部分，它定义了患者是在5年间隔内生存还是死亡。而治疗部分则表示在这段时间内所进行的治疗，包括激素治疗、放射治疗或手术，共分为六类。该模型在每个节点上对一个类与其他类进行分类，这使得基于树的模型创建了五个节点。该模型使用一组基于综合研究数据集的标准分类器进行训练，该数据集包括347名患者的基因组图谱和临床信息。在每个节点上应用特征选择方法和预测方法的组合，以识别可以预测该节点处的类别的基因，针对每个类别识别的基因可以用作类别治疗的潜在生物标志物，以获得更好的存活性。结果表明，该模型识别类与高性能的测量。基于相关文献的详尽分析表明，一些潜在的生物标志物与乳腺癌的生存率和一般癌症密切相关。
Genomic profiles among different breast cancer survivors who received similar treatment may provide clues about the key biological processes involved in the cells and finding the right treatment. More specifically, such profiling may help personalize the treatment based on the patients' gene expression. In this paper, we present a hierarchical machine learning system that predicts the 5-year survivability of the patients who underwent though specific therapy; The classes are built on the combination of two parts that are the survivability information and the given therapy. For the survivability information part, it defines whether the patient survives the 5-years interval or deceased. While the therapy part denotes the therapy has been taken during that interval, which includes hormone therapy, radiotherapy, or surgery, which totally forms six classes. The Model classifies one class vs. the rest at each node, which makes the tree-based model creates five nodes. The model is trained using a set of standard classifiers based on a comprehensive study dataset that includes genomic profiles and clinical information of 347 patients. A combination of feature selection methods and a prediction method are applied on each node to identify the genes that can predict the class at that node, the identified genes for each class may serve as potential biomarkers to the class's treatment for better survivability. The results show that the model identifies the classes with high-performance measurements. An exhaustive analysis based on relevant literature shows that some of the potential biomarkers are strongly related to breast cancer survivability and cancer in general.