Abstract
After reviewing the underlying framework required for computational function prediction in the previous chapter, we discuss two advanced sequence-based function prediction methods developed in our group, namely the Protein Function Prediction (PFP) method and the Extended Similarity Group (ESG) method. PFP extends the traditional homology search by incorporating functional associations between pairs of Gene Ontology terms based on the frequencies of co-occurrences in annotation of the same proteins in the database. PFP also considers very weakly similar sequences to the query, thereby increases its sensitivity and ability to predict low resolution functional terms. On the other hand, ESG recursively searches the sequence similarity space around the query to find consensus annotations in the neighborhood. The last part of the chapter discusses the network structure of gene functional space built by connecting proteins with functional similarity. Function annotation was enriched by predictions by PFP. Similarity to structures of protein-protein interaction networks and metabolic pathway networks is discussed.
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Acknowledgements
MC is supported by grants from Purdue Research Foundation and the Showalter Trust. DK also acknowledges a grant from National Institutes of Health (GM075004) and National Science Foundation (DMS800568, EF0850009, IIS0915801).
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Chitale, M., Kihara, D. (2011). Enhanced Sequence-Based Function Prediction Methods and Application to Functional Similarity Networks. In: Kihara, D. (eds) Protein Function Prediction for Omics Era. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0881-5_2
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DOI: https://doi.org/10.1007/978-94-007-0881-5_2
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