Gram-positive and Gram-negative subcellular localization using rotation forest and physicochemical-based features.
BACKGROUND
The functioning of a protein relies on its location in the cell. Therefore, predicting protein subcellular localization is an important step towards protein function prediction. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve the prediction performance. However, for newly sequenced proteins, the GO is not available. Therefore, for these cases, the prediction performance of GO based methods degrade significantly.


RESULTS
In this study, we develop a method to effectively employ physicochemical and evolutionary-based information in the protein sequence. To do this, we propose segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids to tackle Gram-positive and Gram-negative subcellular localization. We explore our proposed feature extraction techniques using 10 attributes that have been experimentally selected among a wide range of physicochemical attributes. Finally by applying the Rotation Forest classification technique to our extracted features, we enhance Gram-positive and Gram-negative subcellular localization accuracies up to 3.4% better than previous studies which used GO for feature extraction.


CONCLUSIONS
By proposing segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids as well as using Rotation Forest classification technique, we are able to enhance the Gram-positive and Gram-negative subcellular localization prediction accuracies, significantly.
Abdollah
A
Dehzangi
Sohrab
S
Sohrabi
Rhys
R
Heffernan
Alok
A
Sharma
James
J
Lyons
Kuldip
K
Paliwal
Abdul
A
Sattar
D000465:Algorithms; D001426:Bacterial Proteins / Q000378:metabolism*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006090:Gram-Negative Bacteria / Q000302:isolation & purification* / Q000378:metabolism; D006094:Gram-Positive Bacteria / Q000302:isolation & purification* / Q000378:metabolism; D015233:Models, Statistical; D011237:Predictive Value of Tests; D020539:Sequence Analysis, Protein / Q000379:methods*; D013347:Subcellular Fractions
D001426:Bacterial Proteins