Predicting neuron growth is valuable to understand the morphology of neurons, thus it is helpful in the research of neuron classification. This study sought to propose a new method of predicting the growth of human ne...Predicting neuron growth is valuable to understand the morphology of neurons, thus it is helpful in the research of neuron classification. This study sought to propose a new method of predicting the growth of human neurons using 1 907 sets of data in human brain pyramidal neurons obtained from the website of NeuroMorpho.Org. First, we analyzed neurons in a morphology field and used an expectation-maximization algorithm to specify the neurons into six clusters. Second, naive Bayes classifier was used to verify the accuracy of the expectation-maximization algorithm. Experiment results proved that the cluster groups here were efficient and feasible. Finally, a new method to rank the six expectation-maximization algorithm clustered classes was used in predicting the growth of human pyramidal neurons.展开更多
目的乳腺癌风险预测模型可将人群分为不同的风险等级,有助于降低筛查成本,使乳腺癌筛查效益最大化。本研究分析了上海市女性乳腺癌的危险因素,初步建立了符合该人群流行病学特征的风险预测模型,为乳腺癌高危人群的筛选提供依据。方法 20...目的乳腺癌风险预测模型可将人群分为不同的风险等级,有助于降低筛查成本,使乳腺癌筛查效益最大化。本研究分析了上海市女性乳腺癌的危险因素,初步建立了符合该人群流行病学特征的风险预测模型,为乳腺癌高危人群的筛选提供依据。方法 2008-05-23-2012-09-30,采用调查表对上海市闵行区149 577名35~74岁女性开展乳腺癌初筛,内容包括人口学、月经生育史、乳腺疾病史和家族史等信息,具备任一明确定义危险因素者为初筛阳性。将所有对象的个人信息与上海市肿瘤登记系统和生命统计系统进行记录联动,收集2015-06-30前乳腺癌确诊和全死因死亡信息。采用Cox比例风险模型,建立乳腺癌风险预测模型,计算乳腺癌5年发病风险,并采用5折交叉验证法,分别计算期望病例数与观察病例数比值(ratio of the expected to the observed number,E/O)和受试者工作特征曲线下面积(areas under the receiver operating characteristic curve,AUC),评价模型的校准度和区分力。结果经过774 333人年(中位随访人年5.05年)随访,共发现新发乳腺癌病例973例,粗发病率(crude incidence rate,CIR)和年龄标化率(age-standardized incidence rate,ASR)分别为125.66/10万和112.55/10万,初筛阳性者的粗率和标化率分别为133.91/10万和121.83/10万,显著高于初筛阴性者的119.76/10万和106.91/10万。年龄、教育程度、乳腺癌家族史、患重度乳腺小叶增生、有乳房肿块、患乳腺导管内乳头状瘤与乳腺癌呈正向关联,哺乳和月经周期规律与乳腺癌呈负向关联。基于这些因素建立的风险预测模型估计该人群乳腺癌5年绝对发病风险高峰出现在55岁,在0.19%~1.10%之间变化。模型的E/O值为0.98(95%CI为0.92,1.04),AUC为0.596(95%CI为0.538,0.654)。进一步按年龄分层,发现55岁以下组和55岁及以上组的E/O值分别为0.96(0.88,1.03)和1.01(0.91,1.16),AUC分别为0.627(0.514,0.701)和0.567(0.518,0.630)。结论本研究建立的风险评估模型主要基于自我报告的乳腺症状及体征,总体校准度较好,而总体区分力不理想,但在55岁以下女性中有所提高,可用于社区人群尤其是55岁以下人群的乳腺癌风险分级。展开更多
基金supported by the National Natural Science Foundation of China,No.10872069
文摘Predicting neuron growth is valuable to understand the morphology of neurons, thus it is helpful in the research of neuron classification. This study sought to propose a new method of predicting the growth of human neurons using 1 907 sets of data in human brain pyramidal neurons obtained from the website of NeuroMorpho.Org. First, we analyzed neurons in a morphology field and used an expectation-maximization algorithm to specify the neurons into six clusters. Second, naive Bayes classifier was used to verify the accuracy of the expectation-maximization algorithm. Experiment results proved that the cluster groups here were efficient and feasible. Finally, a new method to rank the six expectation-maximization algorithm clustered classes was used in predicting the growth of human pyramidal neurons.
基金美国中华医学基金会(China Medical BoardHPSS 09-991)+1 种基金上海市第四轮公共卫生计划重点学科建设课题(15GWZK0801)上海市自然科学基金青年项目(12ZR1448700)
文摘目的乳腺癌风险预测模型可将人群分为不同的风险等级,有助于降低筛查成本,使乳腺癌筛查效益最大化。本研究分析了上海市女性乳腺癌的危险因素,初步建立了符合该人群流行病学特征的风险预测模型,为乳腺癌高危人群的筛选提供依据。方法 2008-05-23-2012-09-30,采用调查表对上海市闵行区149 577名35~74岁女性开展乳腺癌初筛,内容包括人口学、月经生育史、乳腺疾病史和家族史等信息,具备任一明确定义危险因素者为初筛阳性。将所有对象的个人信息与上海市肿瘤登记系统和生命统计系统进行记录联动,收集2015-06-30前乳腺癌确诊和全死因死亡信息。采用Cox比例风险模型,建立乳腺癌风险预测模型,计算乳腺癌5年发病风险,并采用5折交叉验证法,分别计算期望病例数与观察病例数比值(ratio of the expected to the observed number,E/O)和受试者工作特征曲线下面积(areas under the receiver operating characteristic curve,AUC),评价模型的校准度和区分力。结果经过774 333人年(中位随访人年5.05年)随访,共发现新发乳腺癌病例973例,粗发病率(crude incidence rate,CIR)和年龄标化率(age-standardized incidence rate,ASR)分别为125.66/10万和112.55/10万,初筛阳性者的粗率和标化率分别为133.91/10万和121.83/10万,显著高于初筛阴性者的119.76/10万和106.91/10万。年龄、教育程度、乳腺癌家族史、患重度乳腺小叶增生、有乳房肿块、患乳腺导管内乳头状瘤与乳腺癌呈正向关联,哺乳和月经周期规律与乳腺癌呈负向关联。基于这些因素建立的风险预测模型估计该人群乳腺癌5年绝对发病风险高峰出现在55岁,在0.19%~1.10%之间变化。模型的E/O值为0.98(95%CI为0.92,1.04),AUC为0.596(95%CI为0.538,0.654)。进一步按年龄分层,发现55岁以下组和55岁及以上组的E/O值分别为0.96(0.88,1.03)和1.01(0.91,1.16),AUC分别为0.627(0.514,0.701)和0.567(0.518,0.630)。结论本研究建立的风险评估模型主要基于自我报告的乳腺症状及体征,总体校准度较好,而总体区分力不理想,但在55岁以下女性中有所提高,可用于社区人群尤其是55岁以下人群的乳腺癌风险分级。
