Skip to Main Content
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Highly connected hub proteins play key roles in constituting protein-protein interaction networks and malfunctioning of a set of them can seriously affect working of the entire network. Identifying proteome-wide hub proteins and understanding their roles in protein interactions networks can provide us insights on overall cellular functions and biological processes fundamental to various diseases. This motivates us to develop a novel method to predict hub proteins solely based on amino acid sequences. Fractal dimensions of protein sequences are used to develop a new method to predict hub proteins. Physicochemical parameters of amino acids, obtained from the Amino Acid Index database, are used to map protein sequences into signals and a variant of the box counting method is then used to find the fractal dimensions of these signals. A feed-forward back propagation neural network with these fractal dimensions as input values is constructed to carry out the prediction. The method gives promising degrees of accuracy (72.3%) with sensitivity (71.1%) and specificity (72.4%), when tested with protein sequences of Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. Thus the proposed method enables prediction of hubness property of proteins from their amino acid sequences.