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About the Authors:
Cen Wan
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review & editing
Affiliations Department of Computer Science, University College London, London, United Kingdom, Biomedical Data Science Laboratory, The Francis Crick Institute, London, United Kingdom
Jonathan G. Lees
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft
Affiliation: Institute of Structural and Molecular Biology, University College London, London, United Kingdom
Federico Minneci
Roles Data curation, Methodology, Resources, Software
Affiliation: Department of Computer Science, University College London, London, United Kingdom
Christine A. Orengo
Roles Data curation, Funding acquisition, Investigation, Resources, Software
Affiliation: Institute of Structural and Molecular Biology, University College London, London, United Kingdom
David T. Jones
Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing
* E-mail: [email protected]
Affiliations Department of Computer Science, University College London, London, United Kingdom, Biomedical Data Science Laboratory, The Francis Crick Institute, London, United Kingdom, Institute of Structural and Molecular Biology, University College London, London, United KingdomAbstract
Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.
Author summary
Despite painstaking experimental efforts and the extensive sequence similarity based annotation transfers, less than a half of the fruit fly protein sequences...