序列最优贝叶斯分类的序列抽样 计数数据

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摘要翻译：
高通量技术已成为生物医学应用中比较研究的选择。由于测序成本或接触到感兴趣的生物，样本点数量有限，这就要求开发高效的样本收集，以最大限度地发挥下游统计分析的能力。我们提出了一种在最优贝叶斯分类框架下顺序选择训练样本的方法。该方法专门针对RNA测序计数数据设计，利用了高效的Gibbs采样过程和闭式更新。我们的结果表明，与随机抽样相比，分类精度有所提高。
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英文标题：
《Sequential Sampling for Optimal Bayesian Classification of Sequencing
  Count Data》
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作者：
Ariana Broumand, Siamak Zamani Dadaneh
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最新提交年份：
2018
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  High throughput technologies have become the practice of choice for comparative studies in biomedical applications. Limited number of sample points due to sequencing cost or access to organisms of interest necessitates the development of efficient sample collections to maximize the power of downstream statistical analyses. We propose a method for sequentially choosing training samples under the Optimal Bayesian Classification framework. Specifically designed for RNA sequencing count data, the proposed method takes advantage of efficient Gibbs sampling procedure with closed-form updates. Our results shows enhanced classification accuracy, when compared to random sampling.
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PDF链接：
https://arxiv.org/pdf/1807.0592

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关键词：贝叶斯分类 贝叶斯 Applications Optimization Technologies 闭式 Gibbs 数据 贝叶斯 成为