NDLI: Improvement of protein disorder prediction by brainstorming consensus

Content Provider	IEEE Xplore Digital Library
Author	Banerjee, S. Guha, S. Dutta, A. Dutta, S.
Copyright Year	2015
Description	Author affiliation: Dept. of Electron. & Commun. Eng., Inst. of Eng. & Manage., Kolkata, India (Banerjee, S.) \|\| Dept. of Comput. Sci. & Eng., Batanagar Inst. of Eng. Manage. & Sci., Kolkata, India (Guha, S.; Dutta, A.; Dutta, S.)
Abstract	Deciphering the structure of a protein is essential towards understanding its functions. But there are proteins which undergo arbitrary changes in their tertiary structure. It is possible for a protein to be completely unstructured or be partially unstructured. Even though such proteins, known as intrinsically disordered proteins (IDP), do not have a fixed structure, they have an important biological role to play. We have designed a system which combines the results obtained from other state-of-the-art predictors to arrive at a single prediction. Most of the predictors have their own advantages and disadvantages. In this consensus scheme we have tried to overcome the disadvantages of one predictor by combining it with another. By combining 10 state-of-the art predictors we could achieve an increase of 15% in terms of recall on a dataset of 261 proteins.
Starting Page	1
Ending Page	7
File Size	302366
Page Count	7
File Format	PDF
e-ISBN	9781479969081
DOI	10.1109/IEMCON.2015.7344428
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-15
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Computer science Meta-consensus Protein disorder prediction Machine learning Predictive models N-star consensus Amino acids Protein sequence Nuclear magnetic resonance Homology modelling
Content Type	Text
Resource Type	Article

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