Importance of Dimensionality Reduction in Protein Fold Recognition
Author(s)
Sharma, Alok
Sharma, Ronesh
Dehzangi, Abdollah
Lyons, James
Paliwal, Kuldip
Tsunoda, Tatsuhiko
Griffith University Author(s)
Year published
2015
Metadata
Show full item recordAbstract
Interpreting tertiary structure of a protein has been a crucial task in the field of biosciences. This problem can be addressed by detecting protein folds which is considered as an intermediate step in the tertiary structure prediction. From the perspective of computational sciences, the protein fold recognition can be subdivided in two steps: 1) feature extraction of protein sequences, and 2) identifying extracted features using appropriate classifiers. These steps are important to accurately identify folds of a novel protein sequence. In order to fully characterize a protein sequence, the number of features required is ...
View more >Interpreting tertiary structure of a protein has been a crucial task in the field of biosciences. This problem can be addressed by detecting protein folds which is considered as an intermediate step in the tertiary structure prediction. From the perspective of computational sciences, the protein fold recognition can be subdivided in two steps: 1) feature extraction of protein sequences, and 2) identifying extracted features using appropriate classifiers. These steps are important to accurately identify folds of a novel protein sequence. In order to fully characterize a protein sequence, the number of features required is large and sometimes even unmanageable. This high dimensionality of features is difficult to process using conventional classifiers. Therefore, it is a challenge to develop and apply dimensionality reduction techniques for protein fold recognition. In this paper, we have emphasized the importance of dimensionality reduction techniques (DRTs) for protein fold recognition. To narrate, we have compared the recognition performance without DRT and with DRT on 3 benchmark datasets.
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View more >Interpreting tertiary structure of a protein has been a crucial task in the field of biosciences. This problem can be addressed by detecting protein folds which is considered as an intermediate step in the tertiary structure prediction. From the perspective of computational sciences, the protein fold recognition can be subdivided in two steps: 1) feature extraction of protein sequences, and 2) identifying extracted features using appropriate classifiers. These steps are important to accurately identify folds of a novel protein sequence. In order to fully characterize a protein sequence, the number of features required is large and sometimes even unmanageable. This high dimensionality of features is difficult to process using conventional classifiers. Therefore, it is a challenge to develop and apply dimensionality reduction techniques for protein fold recognition. In this paper, we have emphasized the importance of dimensionality reduction techniques (DRTs) for protein fold recognition. To narrate, we have compared the recognition performance without DRT and with DRT on 3 benchmark datasets.
View less >
Conference Title
2015 2ND ASIA-PACIFIC WORLD CONGRESS ON COMPUTER SCIENCE AND ENGINEERING (APWC ON CSE 2015)
Subject
Chemical engineering not elsewhere classified