摘要： The 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) provided a multidisciplinary forum for computational scientists and experimental biologists to share recent advances on all aspects of intelligent computing, informatics and data science in biology and medicine. ICIBM 2020 was held as a virtual conference on August 9-10, 2020, including four live sessions with forty-one oral presentations over video conferencing. In this special issue, ten high-quality manuscripts were selected after peer-review from seventy-five submissions to represent the medical informatics and decision making aspect of the conference. In this editorial, we briefly summarize these ten selected manuscripts.

关键词： Machine learning   clustering   shaping   polymer nanocomposites  

摘要： Molecular dynamics (MD) simulation has been an invaluable tool for polymer nanocomposites (PNCs) research. MD simulation investigations provide massive data about the movements of PNCs beads on the micro-level. However, there are many challenges involved in correctly understanding and predicting the performance and properties of molecules from these massive MD simulation data. Traditional data-driven techniques are limited due to computational expense, required accuracy, and the availability of high-dimensional PNCs structures. Machine learning can give an effort to process massive data, but not all machine learning methods are available for all occasions;domain knowledge or manual intervention is required. Here, we aim to investigate unsupervised machine learning algorithms to discover clustering and shaping movements of PNCs beads under tension. Cluster Envelope Shaping Method (CESM) was proposed to analyze the clustering of beads and the shaping of clusters based on MD simulation data. The matrix-free PNC based elastomer simulation and Single-Chain Polymer Nanoparticles simulation, which demonstrate two types of deformations, verified the validity of this method.

关键词： coronaviruses   Spike protein   Machine learning   Sequence clustering   Viral host specificity   t-SNE  

摘要： Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F-1 scores, sensitivities and specificities of 0.95-0.99. Importantly, sites identified by Decision Tree correspond to protein regions with known biological importance. These results demonstrate that spike sequences alone can be used to predict host specificity. (C) 2020 Elsevier Inc. All rights reserved.

关键词： 爆裂   隧道火灾   多场耦合   机器学习   数值分析  

摘要： 隧道火灾温升快,峰值温度高,易引起衬砌发生爆裂,严重危害隧道结构承载能力与安全性。研究爆裂过程的传统多场分析方法涉及复杂非线性系统求解,计算时间长,数值稳定性弱。本文利用已有的强耦合热-水-气-力-化学多场分析模型,对大量不同混凝土类型,环境湿度,火荷载条件下的高温混凝土内孔压和温度增长过程展开分析。将结果作为输入信息,导入开发的机器学习方法中,从而利用机器学习方法来归纳学习高温下衬砌爆裂发展规律,从而无须求解方程而可以快速预测评估任意含水量和配比的隧道衬砌在不同火荷载下衬砌爆裂风险。通过与实验结果对比,证明了方法可靠性,研究成果可以有效辅助工程人员对在建与已建隧道快速开展耐火抗火风险评估。

关键词： 避险货币   因子模型   利率平价   随机森林模型   机器学习  

摘要： 今年伊始,新冠疫情在全世界范围内肆虐,对全球经济造成了沉重打击。在当前全球不确定性不断上升的背景下,如果人民币依旧能够持续升值并保持稳健(即成为避险货币),一方面将有效地减少资本外流从而保障我国金融安全与稳定,另一方面也能够极大地提升人民币的国际地位。本文通过一个类因子模型发现,影响一国货币汇率收益与避险价值的既包括本国因素,还包括本国经济联系密切的其他各国因素(本文称其为外国因素)和全球风险因素。随后,本文利用机器学习方法对此进行了实证检验,分析表明:(1)本国与外国因素均会对货币避险价值造成影响,其中利率因素的影响最为明显,而价格水平因素的影响程度则相去甚远;(2)多数基本面因素不会直接地影响货币避险价值,但会通过影响经济基本面的各类因素对其造成显著的间接影响;(3)本国利率水平、金融要素市场建设与资本市场开放情况对于货币避险价值具有重要意义,但其中大多数外国因素对货币避险价值的影响相对较小。

关键词： 加权随机森林   分类预测   microRNA   三阴性乳腺癌  

摘要： 目的基于microRNA组学数据,探讨加权随机森林在三阴性乳腺癌分类预测中的应用,为疾病诊断提供方法学支撑。方法以TCGA乳腺癌数据为例,采用加权随机森林构建三阴性乳腺癌的分类预测模型,并与随机森林、logistic回归、支持向量机、LASSO和岭回归五种模型进行比较。结果通过比较六种模型的5个评价指标,加权随机森林模型的预测性能明显优于其他五种模型,加权随机森林模型的灵敏度为0.852、特异度为0.873、准确度为0.871、AUC值为0.862和G-means值为0.861。结论加权随机森林构建的分类预测模型较好地识别了三阴性乳腺癌患者,可为三阴性乳腺癌的诊断提供方法学上的参考。

关键词： cancer type classification   ensemble method   performance-weighted-voting model   linear regression   single-nucleotide polymorphism  

摘要： Background:With improvements in next-generation DNA sequencing technology,lower cost is needed to collect genetic *** machine learning techniques can be used to help with cancer analysis and ***:We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer *** ensemble system consists of five weak classifiers(logistic regression,SVM,random forest,XGBoost and neural networks).We first used cross-validation to get the predicted results for the five *** weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression *** final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted ***:Using the somatic mutation count of each gene as the input feature,the overall accuracy of the performance-weighted-voting model reached 71.46%,which was significantly higher than the five weak classifiers and two other ensemble models:the hard-voting model and the soft-voting *** addition,by analyzing the predictive pattern of the performance-weighted-voting model,we found that in most cancer types,higher tumor mutational burden can improve overall ***:This study has important clinical significance for identifying the origin of cancer,especially for those where the primary cannot be *** addition,our model presents a good strategy for using ensemble systems for cancer type classification.

关键词： 自然语言处理   机器学习方法   网站   分类方法   检测精度   期望   个体   服务  

摘要： 识别所有客户的服务恢复期望对企业的服务恢复非常重要。为了解决这一问题,本文以汽车投诉网站的期望分类为根据提出了一种新的期望分类方法,并使用基于自然语言处理和机器学习方法检测新的期望分类方法中的个体期望。与原始网站期望分类相比,运用相同的方法进行个体期望检测,我们提出的方法能够得到更好的检测精度。

关键词： 机器学习   灾难生命支持课程   影响因素   Apriori关联规则分析   决策树  

摘要： 2019年底新型冠状病毒(2019-nCoV)肺炎疫情爆发,疫情使人们更加认识到应急救援队伍建设和相关人才培养的重要性。本文以2018—2019年间参加灾难生命支持课程(National Disaster Life Support,NDLS)培训的学员数据为研究对象,利用非监督和监督2种机器学习方法相结合的方式,分析NDLS培训效果的影响因素,帮助培训组织对学员的培训效果进行有效预判,从而提前干预,提高培训质量。首先运用Apriori算法找出若干个对培训效果影响较大的因素,然后用决策树模型对培训效果进行预测,并利用决策树分析的结果验证关联分析的结论。以置信度、支持度及提升度等参数作为Apriori关联规则的评价指标。用十折交叉验证作为决策树预测模型评估的方法。结果显示模型效果良好,其结果可以帮助培训组织对学员的学习效果进行有效预判、监控并保证培训质量。

关键词： SAM   基因表达数据   基因选择   分类预测  

摘要： 目的使用高斯核函数和欧式距离函数改进微阵列显著分析法(significance analysis of microarray,SAM)得到MSAM1法(modified significance analysis of microarray-1,MSAM1)和MSAM2法(modified significance analysis of microarray-2,MSAM2),与SAM法、Relief法、支持向量机递归特征消除法(support vector machine recursive feature elimination, SVM-RFE)进行对比,评价在基因表达数据中MSAM1法、MSAM2法的基因选择和分类预测能力。方法从Bioconductor中的golubEsets包获得leukemia数据集(Golub等人给出了该数据集所包含的50个差异基因),运用R软件实现5种算法,分别用正确率和ROC曲线下面积即AUC值评价基因选择能力和分类预测能力,用Kruskal-Wallis H检验比较5种方法的正确率和AUC值的组间差异,进一步的两两比较采用SNK-q检验。结果正确率和AUC值均表现为MSAM1和MSAM2最优,SAM和SVM-RFE法次之,Relief法排在最后;5种方法的组间差异有统计学意义(H=150.333,P<0.0001和H=293.2579,P<0.0001),两两比较结果显示虽然MSAM1和MSAM2之间差异无统计学意义(P>0.05),但两种方法与其他3种方法之间差异均有统计学意义(P<0.05)。结论用高斯核函数和欧式距离函数改进的加权SAM法提高了SAM法的基因选择和分类预测能力,在实际基因表达数据的应用中可以得到更为稳定的分析结果。