A local average distance descriptor for flexible protein structure comparison
نویسندگان
چکیده
منابع مشابه
Assessing Distance Measures for Protein Structure Comparison∗
Distance measure is a key component for fast protein structure comparison. The identical representation of protein structure combining with different distance measures results in different structure comparison methods. In this paper, we provide and further analyze three structure comparison methods with contact vector representation based on three different distance measures respectively, i.e. ...
متن کاملA Novel Local Structure Descriptor for Color Image Retrieval
A novel local structure descriptor (LSD) for color image retrieval is proposed in this paper. Local structures are defined based on a similarity of edge orientation, and LSD is constructed using the underlying colors in local structures with similar edge direction. LSD can effectively combine color, texture and shape as a whole for image retrieval. LSH integrates the advantages of both statisti...
متن کاملProtein Local Structure Alignment Under the Discrete Fréchet Distance
Protein structure alignment is a fundamental problem in computational and structural biology. While there has been lots of experimental/heuristic methods and empirical results, very few results are known regarding the algorithmic/complexity aspects of the problem, especially on protein local structure alignment. A well-known measure to characterize the similarity of two polygonal chains is the ...
متن کاملLocal Phase Quantization Texture Descriptor for Protein Classification
In this work we propose a method for protein classification based on a texture descriptor, called local phase quantization that utilizes phase information computed from the image extracted from the 3-D tertiary structure of a given protein. To build this texture, the Euclidean distance is calculated between all the atoms that belong to the protein backbone. Moreover, we study classification fus...
متن کاملA novel Local feature descriptor using the Mercator projection for 3D object recognition
Point cloud processing is a rapidly growing research area of computer vision. Introducing of cheap range sensors has made a great interest in the point cloud processing and 3D object recognition. 3D object recognition methods can be divided into two categories: global and local feature-based methods. Global features describe the entire model shape whereas local features encode the neighborhood ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2014
ISSN: 1471-2105
DOI: 10.1186/1471-2105-15-95