Online phenotype discovery based on minimum classification error model

Zheng Yin, Xiaobo Zhou, Youxian Sun, Stephen T C Wong

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)509-522
Number of pages14
JournalPattern Recognition
Issue number4
StatePublished - Apr 2009


  • Gap statistics
  • High content screen
  • Minimum classification error
  • Online phenotype discovery
  • RNA interference

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing


Dive into the research topics of 'Online phenotype discovery based on minimum classification error model'. Together they form a unique fingerprint.

Cite this