Online phenotype discovery based on minimum classification error model

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摘要

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.

论文关键词:Online phenotype discovery,Minimum classification error,RNA interference,High content screen,Gap statistics

论文评审过程:Received 13 March 2008, Revised 10 June 2008, Accepted 12 September 2008, Available online 21 October 2008.

论文官网地址:https://doi.org/10.1016/j.patcog.2008.09.032