MS-k NN: protein function prediction by integrating multiple data sources
نویسندگان
چکیده
منابع مشابه
SMISS: A protein function prediction server by integrating multiple sources
SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network, and the rules mined from protein function associations. SMISS automatically switc...
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Determining protein function constitutes an exercise in integrating information derived from several heterogeneous high-throughput experiments. To utilize the information spread across multiple sources in a combined fashion, these data sources are transformed into kernels. Several protein function prediction methods follow a two-phased approach: they first optimize the weights on individual ker...
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As we are moving into the post genome-sequencing era, various high-throughput experimental techniques have been developed to characterize biological systems at the genome scale. Discovering new biological knowledge from high-throughput biological data is a major challenge for bioinformatics today. To address this challenge, we developed a Bayesian statistical method together with Boltzmann mach...
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MOTIVATION Protein localization data are a valuable information resource helpful in elucidating protein functions. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. RESULTS In this paper, fuzzy k-nearest neighbors (k-NN) algorithm has been introduced to predict proteins' subcellular locations from their dipeptide composition. The prediction i...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2013
ISSN: 1471-2105
DOI: 10.1186/1471-2105-14-s3-s8