TY - JOUR T1 - Protein classification using modified <em>n</em>-<em>gram</em> and <em>skip</em>-<em>gram</em> models JF - bioRxiv DO - 10.1101/170407 SP - 170407 AU - S M Ashiqul Islam AU - Benjamin J Heil AU - Christopher Michel Kearney AU - Erich J Baker Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/07/31/170407.abstract N2 - Motivation Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of N-Gram and Skip-Gram models (m-NGSG).Results A meta-comparison of cross validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein prediction to a broader range of scientists.Availability m-NGSG is freely available at Bitbucket: https://bitbucket.org/smislam/mngsg/srcSupplements link to supplementary documentsContact Erich_Baker{at}baylor.edu ER -