Improved image based protein representations with application to membrane protein type prediction
| Autor: | Concepcion Iribar, Victoria E. Sánchez, Jose M. Peinado, Antonio M. Peinado, Juan A. Morales-Cordovilla, Juan D. Clares |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
chemistry.chemical_classification Computer science business.industry Feature vector Feature extraction A protein Pattern recognition Protein engineering Type (model theory) Machine learning computer.software_genre Texture (geology) Amino acid Set (abstract data type) 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Membrane protein chemistry Artificial intelligence business computer |
| Zdroj: | TSP |
| DOI: | 10.1109/tsp.2017.8076022 |
| Popis: | With the explosion of protein sequences generated in the postgenomic era, there is a need for the development of computational methods to characterize and classify them as an alternative to the experimental methods that are expensive and time consuming. Although the amino acid chains that constitute proteins are originally symbolic chains they can be converted into numerical sequences and processed as signals. One recent approach represents a protein as a set of images derived from numerical representations of the protein based on the physicochemical properties of amino acids. Then a feature vector is extracted from texture descriptors of the set of images. In this paper we adopt the same approach of representing proteins as sets of images but we propose to generate the images from evolutionary or structural characterization of proteins instead of generating them from physicochemical properties. We also propose the use of an alternative texture descriptor that, in combination with the proposed approach, obtains a significant improvement of classification accuracy in a membrane protein type prediction task. |
| Databáze: | OpenAIRE |
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