IEEE/ACM Trans Comput Biol Bioinform. 2022 Sep-Oct;19(5):2547-2559. doi: 10.1109/TCBB.2021.3132339. Epub 2022 Oct 10.
Breast cancer is the second most common cancer type and is the leading cause of cancer-related deaths worldwide. Since it is a heterogeneous disease, subtyping breast cancer plays an important role in performing a specific treatment. Gene expression data is a viable alternative to be employed on cancer subtype classification, as they represent the state of a cell at the molecular level, but generally has a relatively small number of samples compared to a large number of genes. Gene selection is a promising approach that addresses this uneven high-dimensional matrix of genes versus samples and plays an important role in the development of efficient cancer subtype classification. In this work, an innovative outlier-based gene selection (OGS) method is proposed to select relevant genes for efficiently and effectively classify breast cancer subtypes. Experiments show that our strategy presents an F score of 1.0 for basal and 0.86 for her 2, the two subtypes with the worst prognoses, respectively. Compared to other methods, our proposed method outperforms in the F score using 80% less genes. In general, our method selects only a few highly relevant genes, speeding up the classification, and significantly improving the classifier's performance.
乳腺癌是第二常见的癌症类型，也是全球癌症相关死亡的主要原因。由于它是一种异质性疾病，对乳腺癌进行亚型分类在进行特定治疗方面起着重要作用。基因表达数据是癌症亚型分类的一种可行替代方法，因为它们代表了细胞在分子水平上的状态，但与大量基因相比，通常样本数量相对较少。基因选择是一种有前途的方法，可以解决这种不均匀的高维基因与样本矩阵，并在开发有效的癌症亚型分类中发挥重要作用。在这项工作中，提出了一种基于异常值的创新基因选择（OGS）方法，用于选择相关基因，以有效地对乳腺癌亚型进行分类。实验表明，我们的策略分别为基底型和 Her2 型（预后最差的两种亚型）提供了 1.0 的 F 分数。与其他方法相比，我们的方法使用 80%更少的基因在 F 分数上表现更好。总的来说，我们的方法只选择了少数高度相关的基因，从而加快了分类速度，并显著提高了分类器的性能。
