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基于低秩近似的一般性增量矩阵分解框架

黄训蓬
4902

First Order Methods for Fast Linear Programming in SHUFE

邓琪
3308

DataBrain,基于R语言开发的机器学习引擎

海宜真
3633

Targeted Sampling and Pricing Strategy with Imperfect Targetabil

邓世名
3186

高频金融数据的非参数分析方法

徐刚
3418

Consistent Multiple Change-point Detection and R implementation

李亚光
4319

Great Again or Stronger Together? Sentiment Analysis About Book

黎思言
3460

Detection and Tracking

陈天龙
2913

R与深度学习的应用

李舰
3203

眼底图像自动识别与诊断

蒋宇康
4274

Detecting concordance and discordance changes among a series of

赖颖蕾
3445

Smart Monitoring for Complex Diseases by Collaborative Learning

黄帅
3002

“AI+慢性病管理”使精准医疗成为可能

金博
3124

高校创业数据分析

王菲菲
3090

证券分析师的价值分析

周静
3338

基于车联网数据的商业价值探索

周扬
3216

移动程序化广告

陈昱
3293

数据融合与信用风险评估

成慧敏
3007

上证50成分股的“社交网络”

李茂
3453

如何制造一次成功的投资

李翛然
2811

交通大数据分析与可视化

刘丹月
3180

AI * HR:用数据改变招聘

朱琛
4304

R语言在教育大数据上的应用

张弢
2984

大规模线上实验与机器学习

熊熹
3279

A Data-Mining Approach to Identification of Risk Factors in Avia

史东辉
3616

复杂网络置信社团结构挖掘

周旷
3483

社会化行为数据挖掘方法及应用

刘淇
3129

医疗大数据分析

谢金贵
3655

函数型数据的过程分析方法

王占锋
3598

R在客户关系管理中的应用

张渊浩
3138

讯飞大数据的实践与思考

谭昶
3920
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With the current microarray and RNA sequencing technologies, two-sample genome-wide expression data have been increasingly collected in biological and medical studies. Di erential expression analysis and gene set enrichment analysis have been frequently conducted. The related statistical software in R has been widely used. Integrative analysis can be conducted when multiple data sets are available. In practice, concordant and discordant molecular behaviors among a series of data sets can be of biological and clinical interest. There is still a lack of statistical methods and software for these types of integrative analysis. We have proposed a mixture model based approach to the integrative analysis of multiple large-scale two- sample expression data sets. Since the mixture model is based on the transformed di erential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA sequencing platforms. The mixture model is simple with three normal distribution components for each data set to represent down-regulation, up-regulation and no di erential expression. However, when the number of data sets increases, the model parameter space increases exponentially due to the component combination from di erent data sets. To achieve a concordant and discordant integrative analysis for a series of data sets, We have introduced two model reduction strategies. The related statistical computing has been implemented in R. We demonstrate our methods on the recent TCGA RNA sequencing data. To illustrate a concordant integrative analysis, we apply our method to a series of data sets collected for studying two closely related types of cancer. To illustrate a discordant integrative analysis, we apply our method to a series of data sets collected for studying di erent types of cancer. Interesting disease-related pathways can be detected by our integrative analysis approach. 
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