Online Phenotype Discovery based on Minimum Classification Error Model.
Online Phenotype Discovery based on Minimum Classification Error Model.
复制标题
基于最小分类误差模型的在线表型发现。
DOI：
10.1016/j.patcog.2008.09.032
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发表时间：
2009
影响因子：
8
通讯作者：
Wong,StephenTC
中科院分区：
文献类型：
--
作者：
Yin,Zheng;Zhou,Xiaobo;Sun,Youxian;Wong,StephenTC
从在线输入的图像中识别和验证新的表型是对抗高含量RNA干扰(RNAi)筛选的主要挑战。新发现的表型应该在视觉上与现有的表型截然不同，并具有生物学意义。提出了一种基于改进缺口统计量的自适应表型建模和迭代聚类合并的在线表型发现方法。基于紧凑性准则的聚类结果和基于现有表型的高斯混合模型(GMM)通过多假设检验和基于最小分类误差(MCE)的模型优化迭代地相互修改。当应用于合成数据集和带有地面真实标签的RNAi高内容屏幕(HCS)图像时，该方法能够很好地自适应地发现新的表型。
Identifying and validating novel phenotypes from images inputting online is a major challenge against high-content RNA interference (RNAi) screening. Newly discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using improved gap statistics is proposed. Clustering results based on compactness criteria and Gaussian mixture models (GMM) for existing phenotypes iteratively modify each other by multiple hypothesis test and model optimization based on minimum classification error (MCE). The method works well on discovering new phenotypes adaptively when applied to both of synthetic datasets and RNAi high content screen (HCS) images with ground truth labels.