HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network.

Current Genomics
Rahul Semwal, Pritish Kumar Varadwaj

Abstract

To develop a tool that can annotate subcellular localization of human proteins. With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research. To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision. In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset. The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other...Continue Reading

References

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Software Mentioned

- HIT
CD
DeepLoc
sklearn
PSI
PSORTb
R
CLASSIFIER
Sub
RF

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