title: Human cell structure-driven model construction for predicting protein subcellular location from biological images.
abstract: BACKGROUND
  The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting.
  
  
  RESULTS
  We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method.
  
  
  METHODS
  The dataset and code can be downloaded from https://github.com/shaoweinuaa/.
  
  
  BACKGROUND
  dqzhang@nuaa.edu.cn
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Shao
authors.affiliation: School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
authors.forename: Mingxia
authors.initials: M
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
authors.forename: Daoqiang
authors.initials: D
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D002451:Cell Compartmentation; D002477:Cells / Q000378:metabolism*; D030562:Databases, Protein; D006801:Humans; D021621:Imaging, Three-Dimensional; D008954:Models, Biological; D011506:Proteins / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism; D013347:Subcellular Fractions / Q000378:metabolism
chemical_list: D011506:Proteins; D020543:Proteome