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BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins
 
Creator Selvaraj, MuthuKrishnan
Puri, Munish
Dikshit, Kanak L.
Lefevre, Christophe
 
Subject QR Microbiology
 
Description The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.
 
Publisher Hindawi Publishing Cop.
 
Date 2016
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/1843/1/Muthu2016.pdf
Selvaraj, MuthuKrishnan and Puri, Munish and Dikshit, Kanak L. and Lefevre, Christophe (2016) BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins. Advances in Bioinformatics, 2016. pp. 1-11. ISSN 1687-8027
 
Relation http://dx.doi.org/10.1155/2016/8150784
http://crdd.osdd.net/open/1843/