Kunjie Fan1, Yuanfang Guan2, Yan Zhang1,3. 1. Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA. 2. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. 3. The Ohio State University Comprehensive Cancer Center (OSUCCC - James), Columbus, OH 43210, USA.
Abstract
BACKGROUND: Identifying protein functions is important for many biological applications. Since experimental functional characterization of proteins is time-consuming and costly, accurate and efficient computational methods for predicting protein functions are in great demand for generating the testable hypotheses guiding large-scale experiments." RESULTS: Here, we propose Graph2GO, a multi-modal graph-based representation learning model that can integrate heterogeneous information, including multiple types of interaction networks (sequence similarity network and protein-protein interaction network) and protein features (amino acid sequence, subcellular location, and protein domains) to predict protein functions on gene ontology. Comparing Graph2GO to BLAST, as a baseline model, and to two popular protein function prediction methods (Mashup and deepNF), we demonstrated that our model can achieve state-of-the-art performance. We show the robustness of our model by testing on multiple species. We also provide a web server supporting function query and downstream analysis on-the-fly. CONCLUSIONS: Graph2GO is the first model that has utilized attributed network representation learning methods to model both interaction networks and protein features for predicting protein functions, and achieved promising performance. Our model can be easily extended to include more protein features to further improve the performance. Besides, Graph2GO is also applicable to other application scenarios involving biological networks, and the learned latent representations can be used as feature inputs for machine learning tasks in various downstream analyses.
BACKGROUND: Identifying protein functions is important for many biological applications. Since experimental functional characterization of proteins is time-consuming and costly, accurate and efficient computational methods for predicting protein functions are in great demand for generating the testable hypotheses guiding large-scale experiments." RESULTS: Here, we propose Graph2GO, a multi-modal graph-based representation learning model that can integrate heterogeneous information, including multiple types of interaction networks (sequence similarity network and protein-protein interaction network) and protein features (amino acid sequence, subcellular location, and protein domains) to predict protein functions on gene ontology. Comparing Graph2GO to BLAST, as a baseline model, and to two popular protein function prediction methods (Mashup and deepNF), we demonstrated that our model can achieve state-of-the-art performance. We show the robustness of our model by testing on multiple species. We also provide a web server supporting function query and downstream analysis on-the-fly. CONCLUSIONS:Graph2GO is the first model that has utilized attributed network representation learning methods to model both interaction networks and protein features for predicting protein functions, and achieved promising performance. Our model can be easily extended to include more protein features to further improve the performance. Besides, Graph2GO is also applicable to other application scenarios involving biological networks, and the learned latent representations can be used as feature inputs for machine learning tasks in various downstream analyses.
Authors: L J Jensen; R Gupta; N Blom; D Devos; J Tamames; C Kesmir; H Nielsen; H H Staerfeldt; K Rapacki; C Workman; C A F Andersen; S Knudsen; A Krogh; A Valencia; S Brunak Journal: J Mol Biol Date: 2002-06-21 Impact factor: 5.469
Authors: Predrag Radivojac; Wyatt T Clark; Tal Ronnen Oron; Alexandra M Schnoes; Tobias Wittkop; Artem Sokolov; Kiley Graim; Christopher Funk; Karin Verspoor; Asa Ben-Hur; Gaurav Pandey; Jeffrey M Yunes; Ameet S Talwalkar; Susanna Repo; Michael L Souza; Damiano Piovesan; Rita Casadio; Zheng Wang; Jianlin Cheng; Hai Fang; Julian Gough; Patrik Koskinen; Petri Törönen; Jussi Nokso-Koivisto; Liisa Holm; Domenico Cozzetto; Daniel W A Buchan; Kevin Bryson; David T Jones; Bhakti Limaye; Harshal Inamdar; Avik Datta; Sunitha K Manjari; Rajendra Joshi; Meghana Chitale; Daisuke Kihara; Andreas M Lisewski; Serkan Erdin; Eric Venner; Olivier Lichtarge; Robert Rentzsch; Haixuan Yang; Alfonso E Romero; Prajwal Bhat; Alberto Paccanaro; Tobias Hamp; Rebecca Kaßner; Stefan Seemayer; Esmeralda Vicedo; Christian Schaefer; Dominik Achten; Florian Auer; Ariane Boehm; Tatjana Braun; Maximilian Hecht; Mark Heron; Peter Hönigschmid; Thomas A Hopf; Stefanie Kaufmann; Michael Kiening; Denis Krompass; Cedric Landerer; Yannick Mahlich; Manfred Roos; Jari Björne; Tapio Salakoski; Andrew Wong; Hagit Shatkay; Fanny Gatzmann; Ingolf Sommer; Mark N Wass; Michael J E Sternberg; Nives Škunca; Fran Supek; Matko Bošnjak; Panče Panov; Sašo Džeroski; Tomislav Šmuc; Yiannis A I Kourmpetis; Aalt D J van Dijk; Cajo J F ter Braak; Yuanpeng Zhou; Qingtian Gong; Xinran Dong; Weidong Tian; Marco Falda; Paolo Fontana; Enrico Lavezzo; Barbara Di Camillo; Stefano Toppo; Liang Lan; Nemanja Djuric; Yuhong Guo; Slobodan Vucetic; Amos Bairoch; Michal Linial; Patricia C Babbitt; Steven E Brenner; Christine Orengo; Burkhard Rost; Sean D Mooney; Iddo Friedberg Journal: Nat Methods Date: 2013-01-27 Impact factor: 28.547