Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States.
Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States.
Anal Chem. 2021 Jun 8;93(22):7763-7773. doi: 10.1021/acs.analchem.0c04850. Epub 2021 May 24.
The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.
为了更好地理解疾病的发生、发展、诊断和治疗，需要进行整体分子测量，这导致了越来越多的组学分析。然而，多组学评估所提供的丰富信息既需要评估，也需要解释极其庞大的数据集，这限制了分析的通量和采用的便利性。利用人工智能 (AI) 的计算方法为解决这些挑战提供了最有希望的途径，但尽管 AI 具有概念上的优势，并且在单一组学研究中得到了成功的应用，AI 在多组学研究中的广泛应用仍然受到限制。在这里，我们讨论了 AI 技术在多组学研究中的现有和未来能力，同时引入了分析性的制衡措施来验证计算得出的结论。
