A new paradigm for clinical biomarker discovery and screening with Mass Spectrometry through biomedical image analysis principles
A new paradigm for clinical biomarker discovery and screening with Mass Spectrometry through biomedical image analysis principles
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通过生物医学图像分析原理，利用质谱法发现和筛选临床生物标志物的新范例
DOI：
10.1109/isbi.2014.6868123
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Liao H
中科院分区：
文献类型：
--
作者：
Liao H
在顺从地取样的体液中发现生物标志物有可能使临床筛查计划能够早期发现疾病。液相色谱-质谱联用（LC-MS）已成为对这些临床样本中的蛋白质和代谢物进行灵敏和自动分析的核心技术。然而，LC-MS作为发现和筛选的精确和可靠平台的潜力取决于稳健、灵敏和特异的信号提取和解释。LC-MS的输出形成为一组可量化的图像，其中包含在疾病和治疗中调节的数千个生化信号。我们建议解决这个问题的第一次与生物医学图像分析范例。提出了一种新的图像重建、分组图像配准和贝叶斯函数混合效应建模的工作流程。泊松计数噪声和对数正态生物变化在原始图像域中建模，从而显著提高了差分分析的检测限。
Biomarker discovery in amenably sampled body fluids has the potential to empower clinical screening programs for the early detection of disease. Liquid Chromatography interfaced to Mass Spectrometry (LC-MS) has emerged as a central technique for sensitive and automated analysis of proteins and metabolites from these clinical samples. However, the potential of LC-MS as a precise and reliable platform for discovery and screening is dependent on robust, sensitive and specific signal extraction and interpretation. The output of LC-MS is formed as a set of quantifiable images containing thousands of biochemical signals regulated in disease and treatment. We propose to tackle this problem for the first time with a biomedical image analysis paradigm. A novel workflow of image reconstruction, groupwise image registration and Bayesian functional mixed-effects modeling is presented. Poisson counting noise and lognormal biological variation are modeled in the raw image domain, resulting in markedly improved detection limit for differential analysis.1
基于特征提取或功能建模方法的蛋白质组生物标志物发现的统计方法。
影响因子：
0.8
作者：
Morris,JeffreyS
通讯作者：
Morris,JeffreyS
影响因子：
3
作者：
Renard BY;Kirchner M;Steen H;Steen JA;Hamprecht FA
通讯作者：
Hamprecht FA