Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence
融合已知与新生物信息的层级特征选择：识别前列腺癌复发相关基因组特征AI 解读 ↓
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融合已知与新生物信息的层级特征选择：识别前列腺癌复发相关基因组特征
- 研究问题
- 如何在生物通路与调控网络尚不完整时，筛选与前列腺癌术后复发相关的高维分子标志物，并用于风险预测？
- 方法与思路
- 作者将未知网络边视为缺失信息，利用观测数据插补；结合通路间与通路内的层级组惩罚，在半参数加速失效时间模型中研究。
- 材料陈述的贡献
- 摘要称，该方法同时纳入已知与插补所得的生物信息；所报告的模拟和应用显示，预测与特征选择表现有所改善。
- 阅读关注点
- 核对网络边的插补依据、层级惩罚构造及调参方式，尤其关注插补误差如何影响特征选择，以及预测比较的验证设计。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明，不能推断刊本变化；摘要未说明插补有效性的条件、比较细节或改善幅度。
原文摘要
Our work is motivated by a prostate cancer study aimed at identifying mRNA and miRNA biomarkers that are predictive of cancer recurrence after prostatectomy. It has been shown in the literature that incorporating known biological information on pathway memberships and interactions among biomarkers improves feature selection of high-dimensional biomarkers in relation to disease risk. Biological information is often represented by graphs or networks, in which biomarkers are represented by nodes and interactions among them are represented by edges; however, biological information is often not fully known. For example, the role of microRNAs (miRNAs) in regulating gene expression is not fully understood and the miRNA regulatory network is not fully established, in which case new strategies are needed for feature selection. To this end, we treat unknown biological information as missing data (i.e., missing edges in graphs), different from commonly encountered missing data problems where variable values are missing. We propose a new concept of imputing unknown biological information based on observed data and define the imputed information as the novel biological information. In addition, we propose a hierarchical group penalty to encourage sparsity and feature selection at both the pathway level and the within-pathway level, which, combined with the imputation step, allows for incorporation of known and novel biological information. While it is applicable to general regression settings, we develop and investigate the proposed approach in the context of semiparametric accelerated failure time models motivated by our data example. Data application and simulation studies show that incorporation of novel biological information improves performance in risk prediction and feature selection and the proposed penalty outperforms the extensions of several existing penalties.