Automated Negative Gene Ontology Based Functional Predictions for Proteins with UniGOPred

Doğan, Tunca
Rifaioğlu, Ahmet Süreyya
Saidi, Rabi
Martin, Maria Jesus
Atalay, Mehmet Volkan
Atalay, Rengül
Functional annotation of biomolecules in the gene and protein databases is mostly incomplete. This is especially valid for multi-domain proteins. There is a grey area in the protein function data resources, where the truly negative functions and the ones possessed by the protein but have not been discovered or documented yet (i.e. false negatives), reside together. In many cases the information about the functions absent from the target biomolecule can be as important as the assigned functions. It’s possible to resolve a portion of this grey area by predicting the functions that the target proteins most probably do not possess. In this study, we present an approach to produce negative functional annotations for protein sequences, along with regular positive associations. Using this approach, we have developed an automated function prediction tool "UniGOPred". The negative prediction performance (recall) was measured as 0.82 for both MF and BP, and 0.66 for CC GO terms (with prediction scores ≤ 0.3), in cross-validation. To the best of our knowledge, the ability of a protein function prediction method to predict negative functions using sequence features is investigated here for the first time.
Citation Formats
T. Doğan, A. S. Rifaioğlu, R. Saidi, M. J. Martin, M. V. Atalay, and R. Atalay, “Automated Negative Gene Ontology Based Functional Predictions for Proteins with UniGOPred,” presented at the 26th Conference on Intelligent Systems for Molecular Biology (2018), Chicago, Amerika Birleşik Devletleri, 2018, Accessed: 00, 2021. [Online]. Available: