Limits...
CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval.

Karim R, Aziz MM, Shatabda S, Rahman MS, Mia MA, Zaman F, Rakin S - Sci Rep (2015)

Bottom Line: Our proposed methods borrow ideas from the field of computer vision.The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure.Experimental results clearly indicate the superiority of our approach in both running time and accuracy.

View Article: PubMed Central - PubMed

Affiliation: AlEDA Group, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Bangladesh.

ABSTRACT
The number of entries in a structural database of proteins is increasing day by day. Methods for retrieving protein tertiary structures from such a large database have turn out to be the key to comparative analysis of structures that plays an important role to understand proteins and their functions. In this paper, we present fast and accurate methods for the retrieval of proteins having tertiary structures similar to a query protein from a large database. Our proposed methods borrow ideas from the field of computer vision. The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure. Experimental results clearly indicate the superiority of our approach in both running time and accuracy. Our method is readily available for use from this website: http://research.buet.ac.bd:8080/Comograd/.

No MeSH data available.


MCC values for binary classification.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4543952&req=5

f5: MCC values for binary classification.

Mentions: Plotting MCC for binary classification with discrimination at various distance values shows that the MCC graph is convex which indicates that this measure is good for binary classifiers. And MCC have highest value at distance 0.011 which indicates that binary classification at distance 0.011 will give the best classification. Also MCC value there is nearly 0.9 which is very good. From this observation it is clearly evident that the distance measure is an effective one (See Fig. 5).


CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval.

Karim R, Aziz MM, Shatabda S, Rahman MS, Mia MA, Zaman F, Rakin S - Sci Rep (2015)

MCC values for binary classification.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4543952&req=5

f5: MCC values for binary classification.
Mentions: Plotting MCC for binary classification with discrimination at various distance values shows that the MCC graph is convex which indicates that this measure is good for binary classifiers. And MCC have highest value at distance 0.011 which indicates that binary classification at distance 0.011 will give the best classification. Also MCC value there is nearly 0.9 which is very good. From this observation it is clearly evident that the distance measure is an effective one (See Fig. 5).

Bottom Line: Our proposed methods borrow ideas from the field of computer vision.The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure.Experimental results clearly indicate the superiority of our approach in both running time and accuracy.

View Article: PubMed Central - PubMed

Affiliation: AlEDA Group, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Bangladesh.

ABSTRACT
The number of entries in a structural database of proteins is increasing day by day. Methods for retrieving protein tertiary structures from such a large database have turn out to be the key to comparative analysis of structures that plays an important role to understand proteins and their functions. In this paper, we present fast and accurate methods for the retrieval of proteins having tertiary structures similar to a query protein from a large database. Our proposed methods borrow ideas from the field of computer vision. The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure. Experimental results clearly indicate the superiority of our approach in both running time and accuracy. Our method is readily available for use from this website: http://research.buet.ac.bd:8080/Comograd/.

No MeSH data available.