基于转录组生物标志物的乳腺癌发育阶段的精确回归。
An accurate regression of developmental stages for breast cancer based on transcriptomic biomarkers.
机构信息
BioKnow Health Informatics Lab, College of Computer Science & Technology, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, PR China.
BioKnow Health Informatics Lab, College of Software, Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin130012, PR China.
出版信息
Biomark Med. 2019 Jan;13(1):5-15. doi: 10.2217/bmm-2018-0305. Epub 2018 Nov 28.
AIM
Breast cancers at different stages have tremendous differences on both phenotypic and molecular patterns. The developmental stage is an essential factor in the clinical decision of treatment plans, but was usually formulated as a classification problem, which ignored the consecutive relationships among them.
MATERIALS & METHODS: This study proposed a regression-based procedure to detect the stage biomarkers of breast cancers. Biomarkers were detected by the Lasso and Ridge algorithms.
RESULTS & CONCLUSION: A collaboration duet of Lasso and Ridge regression algorithms achieved the best performances, with classification accuracy (Acc) equal to 0.8294 and regression goodness-of-fit (R) equal to 0.7810. The 265 biomarker genes were enriched with the signal peptide-based secretion function with the Bonferroni-corrected p-value equal to 6.9408e-3 and false discovery rate (FDR) equal to 1.1614e-2.
目的
不同阶段的乳腺癌在表型和分子模式上存在巨大差异。发育阶段是临床治疗方案决策的一个重要因素，但通常被制定为分类问题，忽略了它们之间的连续关系。
材料与方法
本研究提出了一种基于回归的方法来检测乳腺癌的阶段生物标志物。使用 Lasso 和 Ridge 算法检测生物标志物。
结果与结论
Lasso 和 Ridge 回归算法的协作二重奏实现了最佳性能，分类准确性（Acc）等于 0.8294，回归拟合优度（R）等于 0.7810。265 个生物标志物基因富集了基于信号肽的分泌功能，Bonferroni 校正后的 p 值等于 6.9408e-3，假发现率（FDR）等于 1.1614e-2。
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