ArticlePattern recognition2019
Three-Dimensional Krawtchouk Descriptors for Protein Local Surface Shape Comparison.
Article in Pattern recognition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network.Molecular diversity · 2022Article
- ContactPFP: Protein function prediction using predicted contact information.Frontiers in bioinformatics · 2022Article
- 2DKD: a toolkit for content-based local image search.Source code for biology and medicine · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
Direct comparison of three-dimensional (3D) objects is computationally expensive due to the need for translation, rotation, and scaling of the objects to evaluate their similarity. In applications of 3D object comparison, often identifying specific local regions of objects is of particular interest. We have recently developed a set of 2D moment invariants based on discrete orthogonal Krawtchouk polynomials for comparison of local image patches. In this work, we extend them to 3D and construct 3D Krawtchouk descriptors (3DKDs) that are invariant under translation, rotation, and scaling. The new descriptors have the ability to extract local features of a 3D surface from any region-of-interest. This property enables comparison of two arbitrary local surface regions from different 3D objects. We present the new formulation of 3DKDs and apply it to the local shape comparison of protein surfaces in order to predict ligand molecules that bind to query proteins.
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Registered trials
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