Baciu Cristina, Xu Cherry, Alim Mouaid, Prayitno Khairunnadiya, Bhat Mamatha
Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
Front Artif Intell. 2022 Nov 15;5:1050439. doi: 10.3389/frai.2022.1050439. eCollection 2022.
Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793-0.8838 compared to 0.6759-0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting.
生物技术的快速发展导致了大量多组学数据的产生，这就需要推进生物信息学和人工智能，以实现能够诊断和预测临床结果的计算建模。传统机器学习算法和新的深度学习算法都会无偏地筛选现有数据，以发现规律并创建对临床决策有价值的模型。我们总结了已发表的关于使用在组学数据集上训练的人工智能模型（有或没有临床数据）来诊断、进行风险分层以及预测非恶性肝脏疾病患者生存能力的文献。在选定的研究中总共测试了20种不同的模型。一般来说，将组学数据添加到常规临床参数或单个生物标志物中可提高人工智能模型的性能。例如，使用非酒精性脂肪性肝病纤维化评分来区分F0 - F2和F3 - F4纤维化阶段，曲线下面积（AUC）为0.87。当通过广义多变量学习矢量量化（GMLVQ）模型整合代谢组学数据时，AUC大幅提高到0.99。在另一项研究中，使用随机森林（RF）对多组学和临床数据进行分析以预测非酒精性脂肪性肝病向非酒精性脂肪性肝炎的进展，得到的AUC为0.84，而仅使用临床数据时为0.82。在慢性乙型肝炎的基因组学数据上比较随机森林、支持向量机（SVM）和k近邻（kNN）模型对免疫耐受期进行分类，得到的AUC为0.8793 - 0.8838，而使用各种血清生物标志物时为0.6759 - 0.7276。总体而言，与仅基于临床参数构建的模型相比，组学整合显示出可提高预测性能，这表明在临床环境中个性化医疗具有潜在用途。
