Protein Function Prediction by Clustering of Protein-Protein Interaction Network
Date Issued
2011-09-14
Author(s)
Abstract
The recent advent of high throughput methods has generated large
amounts of protein-protein interaction network (PPIN) data. When studying the
workings of a biological cell, it is useful to be able to detect known and predict
still undiscovered protein complexes within the cell's PPINs. Such predictions
may be used as an inexpensive tool to direct biological experiments. Because of
its importance in the studies of protein interaction network, there are different
models and algorithms in identifying functional modules in PPINs. In this
paper, we present two representative methods, focusing on the comparison of
their clustering properties in PPIN and their contribution towards function
prediction. The work is done with PPIN data from the bakers’ yeast
(Saccaromyces cerevisiae) and since the network is noisy and still incomplete,
we use pre-processing and purifying. As a conclusion new progress and future
research directions are discussed.
amounts of protein-protein interaction network (PPIN) data. When studying the
workings of a biological cell, it is useful to be able to detect known and predict
still undiscovered protein complexes within the cell's PPINs. Such predictions
may be used as an inexpensive tool to direct biological experiments. Because of
its importance in the studies of protein interaction network, there are different
models and algorithms in identifying functional modules in PPINs. In this
paper, we present two representative methods, focusing on the comparison of
their clustering properties in PPIN and their contribution towards function
prediction. The work is done with PPIN data from the bakers’ yeast
(Saccaromyces cerevisiae) and since the network is noisy and still incomplete,
we use pre-processing and purifying. As a conclusion new progress and future
research directions are discussed.
Subjects
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