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An ensemble method with hybrid features to identify extracellular matrix proteins.

Yang R, Zhang C, Gao R, Zhang L - PLoS ONE (2015)

Bottom Line: Moreover, when tested on a common independent dataset, our method also achieves significantly improved performance over ECMPP and ECMPRED.These results indicate that IECMP is an effective method for ECM protein prediction, which has a more balanced prediction capability for positive and negative samples.It is anticipated that the proposed method will provide significant information to fully decipher the molecular mechanisms of ECM-related biological processes and discover candidate drug targets.

View Article: PubMed Central - PubMed

Affiliation: School of Control Science and Engineering, Shandong University, Jinan, China.

ABSTRACT
The extracellular matrix (ECM) is a dynamic composite of secreted proteins that play important roles in numerous biological processes such as tissue morphogenesis, differentiation and homeostasis. Furthermore, various diseases are caused by the dysfunction of ECM proteins. Therefore, identifying these important ECM proteins may assist in understanding related biological processes and drug development. In view of the serious imbalance in the training dataset, a Random Forest-based ensemble method with hybrid features is developed in this paper to identify ECM proteins. Hybrid features are employed by incorporating sequence composition, physicochemical properties, evolutionary and structural information. The Information Gain Ratio and Incremental Feature Selection (IGR-IFS) methods are adopted to select the optimal features. Finally, the resulting predictor termed IECMP (Identify ECM Proteins) achieves an balanced accuracy of 86.4% using the 10-fold cross-validation on the training dataset, which is much higher than results obtained by other methods (ECMPRED: 71.0%, ECMPP: 77.8%). Moreover, when tested on a common independent dataset, our method also achieves significantly improved performance over ECMPP and ECMPRED. These results indicate that IECMP is an effective method for ECM protein prediction, which has a more balanced prediction capability for positive and negative samples. It is anticipated that the proposed method will provide significant information to fully decipher the molecular mechanisms of ECM-related biological processes and discover candidate drug targets. For public access, we develop a user-friendly web server for ECM protein identification that is freely accessible at http://iecmp.weka.cc.

No MeSH data available.


Related in: MedlinePlus

The predicted result page of the IECMP web-server.The predicted result page returns the input information, predicted result, and values of attributes for every submitted sequence.
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pone.0117804.g007: The predicted result page of the IECMP web-server.The predicted result page returns the input information, predicted result, and values of attributes for every submitted sequence.

Mentions: To make it easy for public to access and utilize the method presented in this paper, an IECMP web-server has been launched and is freely available at http://iecmp.weka.cc. The main page of the IECMP web-server is shown in Fig. 6, while the predicted result page is shown in Fig. 7. As displayed in Fig. 6, users can either enter the sequence of query proteins in FASTA format or input the UniProtKB ID of the query protein for prediction. When protein sequences are submitted to the server, a job ID is presented to users. The predicted result page as shown in Fig. 7 will return the input information, predicted result, and values of attributes for every submitted sequence. If users enter your email address in the input box, predicted results will be emailed to users once the job has completed.


An ensemble method with hybrid features to identify extracellular matrix proteins.

Yang R, Zhang C, Gao R, Zhang L - PLoS ONE (2015)

The predicted result page of the IECMP web-server.The predicted result page returns the input information, predicted result, and values of attributes for every submitted sequence.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4334504&req=5

pone.0117804.g007: The predicted result page of the IECMP web-server.The predicted result page returns the input information, predicted result, and values of attributes for every submitted sequence.
Mentions: To make it easy for public to access and utilize the method presented in this paper, an IECMP web-server has been launched and is freely available at http://iecmp.weka.cc. The main page of the IECMP web-server is shown in Fig. 6, while the predicted result page is shown in Fig. 7. As displayed in Fig. 6, users can either enter the sequence of query proteins in FASTA format or input the UniProtKB ID of the query protein for prediction. When protein sequences are submitted to the server, a job ID is presented to users. The predicted result page as shown in Fig. 7 will return the input information, predicted result, and values of attributes for every submitted sequence. If users enter your email address in the input box, predicted results will be emailed to users once the job has completed.

Bottom Line: Moreover, when tested on a common independent dataset, our method also achieves significantly improved performance over ECMPP and ECMPRED.These results indicate that IECMP is an effective method for ECM protein prediction, which has a more balanced prediction capability for positive and negative samples.It is anticipated that the proposed method will provide significant information to fully decipher the molecular mechanisms of ECM-related biological processes and discover candidate drug targets.

View Article: PubMed Central - PubMed

Affiliation: School of Control Science and Engineering, Shandong University, Jinan, China.

ABSTRACT
The extracellular matrix (ECM) is a dynamic composite of secreted proteins that play important roles in numerous biological processes such as tissue morphogenesis, differentiation and homeostasis. Furthermore, various diseases are caused by the dysfunction of ECM proteins. Therefore, identifying these important ECM proteins may assist in understanding related biological processes and drug development. In view of the serious imbalance in the training dataset, a Random Forest-based ensemble method with hybrid features is developed in this paper to identify ECM proteins. Hybrid features are employed by incorporating sequence composition, physicochemical properties, evolutionary and structural information. The Information Gain Ratio and Incremental Feature Selection (IGR-IFS) methods are adopted to select the optimal features. Finally, the resulting predictor termed IECMP (Identify ECM Proteins) achieves an balanced accuracy of 86.4% using the 10-fold cross-validation on the training dataset, which is much higher than results obtained by other methods (ECMPRED: 71.0%, ECMPP: 77.8%). Moreover, when tested on a common independent dataset, our method also achieves significantly improved performance over ECMPP and ECMPRED. These results indicate that IECMP is an effective method for ECM protein prediction, which has a more balanced prediction capability for positive and negative samples. It is anticipated that the proposed method will provide significant information to fully decipher the molecular mechanisms of ECM-related biological processes and discover candidate drug targets. For public access, we develop a user-friendly web server for ECM protein identification that is freely accessible at http://iecmp.weka.cc.

No MeSH data available.


Related in: MedlinePlus