Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computing a family of skeletons of volumetric models for shape description
Computer-Aided Design
New Algorithms for Efficient High-Dimensional Nonparametric Classification
The Journal of Machine Learning Research
Computational Approaches for Automatic Structural Analysis of Large Biomolecular Complexes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Automatic ultrastructure segmentation of reconstructed CryoEM maps of icosahedral viruses
IEEE Transactions on Image Processing
Beta-sheet Detection and Representation from Medium Resolution Cryo-EM Density Maps
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Accurate identification of protein secondary structures is beneficial to understand three-dimensional structures of biological macromolecules. In this paper, a novel refined classification framework is proposed, which treats alpha-helix identification as a machine learning problem by representing each voxel in the density map with its Spherical Harmonic Descriptors (SHD). An energy function is defined to provide statistical analysis of its identification performance, which can be applied to all the \alpha-helix identification approaches. Comparing with other existing \alpha-helix identification methods for intermediate resolution electron density maps, the experimental results demonstrate that our approach gives the best identification accuracy and is more robust to the noise.