Kim Jinwoo, Shin Miyoung
Bio-Intelligence & Data Mining Lab, School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea.
Int J Mol Sci. 2017 Apr 2;18(4):755. doi: 10.3390/ijms18040755.
The effective development of new drugs relies on the identification of genes that are related to the symptoms of toxicity. Although many researchers have inferred toxicity markers, most have focused on discovering toxicity occurrence markers rather than toxicity severity markers. In this study, we aimed to identify gene markers that are relevant to both the occurrence and severity of toxicity symptoms. To identify gene markers for each of four targeted liver toxicity symptoms, we used microarray expression profiles and pathology data from 14,143 in vivo rat samples. The gene markers were found using sparse linear discriminant analysis (sLDA) in which symptom severity is used as a class label. To evaluate the inferred gene markers, we constructed regression models that predicted the severity of toxicity symptoms from gene expression profiles. Our cross-validated results revealed that our approach was more successful at finding gene markers sensitive to the aggravation of toxicity symptoms than conventional methods. Moreover, these markers were closely involved in some of the biological functions significantly related to toxicity severity in the four targeted symptoms.
新药的有效研发依赖于对与毒性症状相关基因的识别。尽管许多研究人员已经推断出毒性标志物，但大多数人专注于发现毒性发生标志物，而非毒性严重程度标志物。在本研究中，我们旨在识别与毒性症状的发生和严重程度均相关的基因标志物。为了识别四种目标肝脏毒性症状各自的基因标志物，我们使用了来自14143个体内大鼠样本的微阵列表达谱和病理数据。通过将症状严重程度用作类别标签的稀疏线性判别分析（sLDA）来发现基因标志物。为了评估推断出的基因标志物，我们构建了从基因表达谱预测毒性症状严重程度的回归模型。我们的交叉验证结果表明，与传统方法相比，我们的方法在发现对毒性症状加重敏感的基因标志物方面更为成功。此外，这些标志物密切参与了与四种目标症状中与毒性严重程度显著相关的一些生物学功能。
