Fukushima Arika, Sugimoto Masahiro, Hiwa Satoru, Hiroyasu Tomoyuki
Graduate School of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan.
Research and Development Center for Minimally Invasive Therapies, Institute of Medical Science, Tokyo Medical University, Shinjuku, Tokyo, 160-8402, Japan.
BMC Bioinformatics. 2021 Mar 18;22(1):132. doi: 10.1186/s12859-021-04052-4.
Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient's response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary.
We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS.
The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.
在整个治疗期间收集的时程数据所提供的历史信息和更新信息，被证明比单点数据所提供的信息更有用。利用多个生物标志物的时程数据做出的准确预测，这些生物标志物表明患者对治疗的反应，对与设计针对各种疾病的有效治疗方案相关的决策过程有积极贡献。因此，开发纳入多个标志物时程数据的预测方法是必要的。
我们提出了可通过时程基因表达谱用于预测和基因选择的新方法。我们的预测方法整合了使用在一系列时间点收集的基因表达谱计算出的多个概率，以预测治疗反应。使用从丙型肝炎病毒（HCV）感染患者和多发性硬化症（MS）患者收集的两个数据集，我们进行了预测治疗反应并评估其准确性的数值实验。我们的方法比传统方法更准确，并成功选择了其功能与HCV感染和MS的病理学相关的基因。
所提出的方法使用多个时间点的数据准确预测了治疗反应。与传统方法相比，它在早期时间点显示出更高的准确性。此外，该方法成功选择了与疾病直接相关的基因。
