title: Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening.
abstract: BACKGROUND
  Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast cancer progression is associated with a defect in EGFR endocytosis. In order to find related Ribonucleic acid (RNA) regulators in this process, high-throughput imaging with fluorescent markers is used to visualize the complex EGFR endocytosis process. Subsequently a dedicated automatic image and data analysis system is developed and applied to extract the phenotype measurement and distinguish different developmental episodes from a huge amount of images acquired through high-throughput imaging. For the image analysis, a phenotype measurement quantifies the important image information into distinct features or measurements. Therefore, the manner in which prominent measurements are chosen to represent the dynamics of the EGFR process becomes a crucial step for the identification of the phenotype. In the subsequent data analysis, classification is used to categorize each observation by making use of all prominent measurements obtained from image analysis. Therefore, a better construction for a classification strategy will support to raise the performance level in our image and data analysis system.
  
  
  RESULTS
  In this paper, we illustrate an integrated analysis method for EGFR signalling through image analysis of microscopy images. Sophisticated wavelet-based texture measurements are used to obtain a good description of the characteristic stages in the EGFR signalling. A hierarchical classification strategy is designed to improve the recognition of phenotypic episodes of EGFR during endocytosis. Different strategies for normalization, feature selection and classification are evaluated.
  
  
  CONCLUSIONS
  The results of performance assessment clearly demonstrate that our hierarchical classification scheme combined with a selected set of features provides a notable improvement in the temporal analysis of EGFR endocytosis. Moreover, it is shown that the addition of the wavelet-based texture features contributes to this improvement. Our workflow can be applied to drug discovery to analyze defected EGFR endocytosis processes.
authors.affiliation: Imaging and Bio-informatics group, LIACS, Leiden University, Niels Bohrweg 1, Leiden, 2333, CA, The Netherlands.|The Department of Anatomy and Embryology, LUMC, Einthovenweg 20, Leiden, 2333, ZC, The Netherlands.
authors.forename: Lu
authors.initials: L
authors.lastname: Cao
authors.affiliation: Division of Toxicology, LACDR, Leiden University, Einsteinweg 55, Leiden, 2333, CC, The Netherlands.
authors.forename: Marjo de
authors.initials: Md
authors.lastname: Graauw
authors.affiliation: Imaging and Bio-informatics group, LIACS, Leiden University, Niels Bohrweg 1, Leiden, 2333, CA, The Netherlands.
authors.forename: Kuan
authors.initials: K
authors.lastname: Yan
authors.affiliation: Biomechanics Laboratory, Erasmus MC, Wytemaweg 80, Rotterdam, 3015, CN, The Netherlands.
authors.forename: Leah
authors.initials: L
authors.lastname: Winkel
authors.affiliation: Imaging and Bio-informatics group, LIACS, Leiden University, Niels Bohrweg 1, Leiden, 2333, CA, The Netherlands. f.j.verbeek@liacs.leidenuniv.nl.
authors.forename: Fons J
authors.initials: FJ
authors.lastname: Verbeek
mesh_terms: D001943:Breast Neoplasms / Q000145:classification / Q000378:metabolism*; D004705:Endocytosis / Q000502:physiology*; D066246:ErbB Receptors / Q000378:metabolism*; D005260:Female; D057166:High-Throughput Screening Assays; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D010641:Phenotype; D015398:Signal Transduction; D060388:Support Vector Machine; D014407:Tumor Cells, Cultured
chemical_list: C512478:EGFR protein, human; D066246:ErbB Receptors
keywords: EGFR endocytosis; Hierarchical classification; High throughput; Image analysis; Phenotype measurement; Wavelet-based texture measurement