Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Bioinformatics
A reliable method for cell phenotype image classification
Artificial Intelligence in Medicine
Accurate sequence-based prediction of catalytic residues
Bioinformatics
Description of interest regions with local binary patterns
Pattern Recognition
Fusion of systems for automated cell phenotype image classification
Expert Systems with Applications: An International Journal
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining knowledge for HEp-2 cell image classification
Artificial Intelligence in Medicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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The reproductive system is a specific system of organs working together for the purpose of reproduction. As one of the most significant characteristics of human cell, subcellular localization plays a critical role for understanding specific functions of mammalian proteins. In this study, we developed a novel computational protocol for predicting protein subcellular locations from microscope images of cells in human reproductive tissues. Three major steps are contained in this protocol, i.e., protein object identification, image feature extraction, and classification. We first separated protein and DNA staining in the images with both linear and non-negative matrix factorization separation methods; then we extracted protein multi-view global and local texture features including wavelet Haralick, local binary patterns, local ternary patterns, and the local quinary patterns; finally based on the selected important feature subset, we constructed an ensemble classifier with support vector machines for classifications. Experiments are performed on a benchmark dataset consisting of seven major subcellular classes in human reproductive tissues collected from human protein atlas. Our results show that the local texture pattern features play an important complementary role to global features for enhancing the prediction performance. An overall accuracy of 85% is obtained through current system, and when only confident classifications are considered, the accuracy can reach 99%. It is the first developed image based protein subcellular location predictor specifically for human reproductive tissue. The promising results indicate that the developed protocol can be applied for accurate large-scale protein subcellular localization annotations in human reproductive system.