Literature DB >> 19136362

Analysis of protein phosphorylation site predictors with an independent dataset.

Abdur R Sikder1, Albert Y Zomaya.   

Abstract

Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental detection of protein phosphorylation sites is labour intensive and often limited by the availability and optimisation of enzymatic reactions. The in silico prediction of phosphorylation sites using protein's primary sequences may provide guidelines for further experimental consideration and interpretation of phosphoproteomic data. An array of such tools exists over the internet and provides the prediction for protein kinase families. We developed an independent dataset to compare the performances of these methods to provide scientists with a better understanding of which method to use for their research.

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Year:  2009        PMID: 19136362     DOI: 10.1504/IJBRA.2009.022461

Source DB:  PubMed          Journal:  Int J Bioinform Res Appl        ISSN: 1744-5485


  2 in total

1.  Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information.

Authors:  Ashis Kumer Biswas; Nasimul Noman; Abdur Rahman Sikder
Journal:  BMC Bioinformatics       Date:  2010-05-21       Impact factor: 3.169

2.  Probabilistic Prediction of Protein Phosphorylation Sites Using Classification Relevance Units Machines.

Authors:  Mark Menor; Kyungim Baek; Guylaine Poisson
Journal:  ACM SIGAPP Appl Comput Rev       Date:  2012-12-01
  2 in total

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