An efficient distributed protein disorder prediction with pasted samples. (January 2018)

• Record Type: Journal Article
• Authors: Smith, Denson; Yenduri, Sumanth; Iqbal, Sumaiya; Krishna, P. Venkata
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 342
• Page End: 356
• Publication Date: 2018-01
• Subjects: Protein disorder classification -- Big data -- Pasting -- Statistical query learning -- Machine learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, we compare prediction performance of a machine learning classifier constructed at once in memory with an ensemble of models constructed with the pasting procedure for protein disorder prediction. The pasting procedure takes sample bites of the training data as input, constructs a classification predictor on each sample and pastes the predictors together. This method has not been previously tested on protein structure data. With a sufficiently large sample size we observed increased performance for the pasting procedure compared with a single model constructed at once in memory for all window sizes. We attribute this increased performance to the robustness of the statistical query learning model. This procedure provides a means to improve classification performance at the protein disorder prediction task as well as construct models too large to be held at once in memory. Graphical abstract: In this paper, we compare prediction performance of a machine learning classifier constructed at once in memory with an ensemble of models constructed with the pasting procedure for protein disorder prediction. The pasting procedure takes sample bites of the training data as input, constructs a classification predictor on each sample and pastes the predictors together.