个性化和精准医学的长期目标是实现对患有疾病的患者的治疗方案结果的准确预测。目前，由于患者群体中存在的基本因素导致对感兴趣药物的反应较差或治疗相关不良事件，许多临床试验未能达到预期目标。事先识别这些因素并对其进行修正可以提高临床试验的成功率。通过对健康和疾病个体进行组学分析的综合和大规模数据收集工作，得到了有助于治疗疾病的药物的疗效的宿主、疾病和环境因素的宝库。随着组学数据的增加，人工智能可以对大数据进行深入分析，并在临床实践中提供广泛的应用，包括改进患者选择和鉴定可行的伴随治疗靶点，以提高更多患者的可转化性。作为复杂的药物-疾病-宿主相互作用的蓝图，我们在这里讨论利用组学数据预测免疫检查点抑制剂（ICIs）在癌症免疫疗法中的反应和不良事件的挑战。对于改善患者结果的基于组学的方法，如ICIs个案，也已在各种复杂疾病环境中应用，展示了组学在深入疾病分析和临床应用中的用途。© 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.© 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.