Print Email Facebook Twitter Automatic gene function prediction in the 2020’s Title Automatic gene function prediction in the 2020’s Author Makrodimitris, S. (TU Delft Pattern Recognition and Bioinformatics; Keygene N.V.) van Ham, R.C.H.J. (TU Delft Pattern Recognition and Bioinformatics; Keygene N.V.) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Date 2020 Abstract The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology. Subject Automatic function predictionGene OntologyMachine learningProtein representation To reference this document use: http://resolver.tudelft.nl/uuid:60e2dfcb-83bd-4cf0-a8fa-96d37b1ae5c3 DOI https://doi.org/10.3390/genes11111264 ISSN 2073-4425 Source Genes, 11 (11), 1-18 Part of collection Institutional Repository Document type review Rights © 2020 S. Makrodimitris, R.C.H.J. van Ham, M.J.T. Reinders Files PDF genes_11_01264_v2.pdf 988.35 KB Close viewer /islandora/object/uuid:60e2dfcb-83bd-4cf0-a8fa-96d37b1ae5c3/datastream/OBJ/view