There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and ‘omic’ technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts.

物质使用障碍 (SUD) 的生物标志物有限。传统的统计方法是在大样本中识别简单的生物标志物，但临床用例仍在建立中。高通量临床、成像和“组学”技术正在从 SUD 研究中生成数据，并可能产生更复杂和临床有用的模型。然而，适合高维数据的分析策略并不经常使用。我们回顾了从高维数据类型中识别生物标志物和生物特征的策略。我们重点关注惩罚回归和贝叶斯方法，以尼古丁代谢为例，讨论如何利用现有研究和知识库的证据。我们认为大数据和机器学习方法将大大推进 SUD 生物标志物的发现。然而，转化为临床实践需要综合科学努力。