An Introduction to Metabolic Networks and Their Structural Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Prediction of protein functions based on function-function correlation relations
Computers in Biology and Medicine
Topological analysis of structural roles of proteins in interactome networks
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Learning functional linkage networks with a cost-sensitive approach
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Fuzzy integral based data fusion for protein function prediction
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
COSNet: a cost sensitive neural network for semi-supervised learning in graphs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Link prediction for annotation graphs using graph summarization
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
A Bayesian integration model for improved gene functional inference from heterogeneous data sources
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
From information networks to bisociative information networks
Bisociative Knowledge Discovery
A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Active learning for protein function prediction in protein-protein interaction networks
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Predicting Protein Functions from Protein Interaction Networks
International Journal of Knowledge Discovery in Bioinformatics
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Motivation: With the increasing availability of diverse biological information, protein function prediction approaches have converged towards integration of heterogeneous data. Many adapted existing techniques, such as machine-learning and probabilistic methods, which have proven successful on specific data types. However, the impact of these approaches is hindered by a couple of factors. First, there is little comparison between existing approaches. This is in part due to a divergence in the focus adopted by different works, which makes comparison difficult or even fuzzy. Second, there seems to be over-emphasis on the use of computationally demanding machine-learning methods, which runs counter to the surge in biological data. Analogous to the success of BLAST for sequence homology search, we believe that the ability to tap escalating quantity, quality and diversity of biological data is crucial to the success of automated function prediction as a useful instrument for the advancement of proteomic research. We address these problems by: (1) providing useful comparison between some prominent methods; (2) proposing Integrated Weighted Averaging (IWA)—a scalable, efficient and flexible function prediction framework that integrates diverse information using simple weighting strategies and a local prediction method. The simplicity of the approach makes it possible to make predictions based on on-the-fly information fusion. Results: In addition to its greater efficiency, IWA performs exceptionally well against existing approaches. In the presence of cross-genome information, which is overwhelming for existing approaches, IWA makes even better predictions. We also demonstrate the significance of appropriate weighting strategies in data integration. Contact: hnchua@i2r.a-star.edu.sg Supplementary information: Supplementary data are available at Bioinformatics online.