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(2016) used hash tables to store essential information learned from GO DAG and to efficiently compute the semantic similarity of genes. Hence, more efficient and effective models are still welcomed. Predicting protein functions using incomplete hierarchical labels. Table 1. ITSS (Tao et al., 2007), dRW (Yu et al., 2015d), HashGO (Yu et al., 2017e), HPHash (Zhao et al., 2019a), and NMFGO (Yu et al., 2020b) are some representative methods introduced in sections 3.1.2, 3.2.2. doi: 10.1371/journal.pcbi.1003073, Chicco, D., Sadowski, P., and Baldi, P. (2014). doi: 10.1093/bioinformatics/btx794, Fu, G., Wang, J., Yang, B., and Yu, G. (2016a). Using the gene ontology hierarchy when predicting gene function? in Conference on Uncertainty in Artificial Intelligence (Montreal, QC), 419427. Semantic similarity in biomedical ontologies. doi: 10.1016/j.compbiolchem.2016.09.005, Lu, M., Zhang, Q., Deng, M., Miao, J., Guo, Y., Gao, W., et al. Several studies investigate the quality of GO annotations from the perspective of evidence codes. Finally, it reconstructed the association matrix using the optimized two low-rank matrices to predict gene functions. In summary, although various computational methods based on GO have been proposed, there are still promising topics and challenges that deserve further efforts. The negative examples selected by ALBias can boost the performance of gene function predictions. Wang, K., Wang, J., Domeniconi, C., Zhang, X., and Yu, G. (2020). Incorporating functional inter-relationships into protein function prediction algorithms. (2015). The True Path Rule is one of the most important rules in GO (Blake, 2013), and should be respected in gene function prediction. Predicting protein function via semantic integration of multiple networks. Although several semantic similarity-based solutions make specific use of the GO hierarchy, GO annotations (Tao et al., 2007; Done et al., 2010; Xu et al., 2013; Yu et al., 2015b,d) and additional data sources (Peng et al., 2018; Yu et al., 2020b) to obtain an improved performance, they are mostly based on the assumption of complete annotations. Valentini (2011) and Cesa-Bianchi et al. 1), 115. Nucleic Acids Res. Hvidsten, T. R., Komorowski, J., Sandvik, A. K., and Laegreid, A. Although some of them also use the annotations augmented by True Path Rule, they still do not explicitly include the important hierarchical inter-relations among the GO terms. Given the incomplete functional knowledge of genes, we have to admit that existing gene function prediction solutions are still no substitute for wet-lab experiments. IEEE/ACM Trans. doi: 10.1186/gb-2008-9-s1-s6. To address this problem, some paradigms (Ashburner et al., 2000; Ruepp et al., 2004; Dessimoz and kunca, 2017) aim to describe the functional properties of gene products in a formal and species neutral way, as well as to assist computational gene function prediction. Front. IEEE/ACM Trans. doi: 10.1101/gr.8.3.163. Genome Biol. Therefore, semantic-based (and also sequence similarity- or interaction network-based) gene function prediction has been popular in recent years (Tao et al., 2007; Yu et al., 2015d, 2016a, 2017c,e). doi: 10.1016/j.ygeno.2013.04.010, Xuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., and Dong, Y. (2009). doi: 10.1093/nar/25.17.3389, Zhao, Y., Fu, G., Wang, J., Guo, M., and Yu, G. (2019a). 5:e1000443. PLoS Comput. (2008) focused on calibrating and combining independent predictions to obtain a set of probabilistic predictions that are consistent with the topology of the ontology. doi: 10.1186/1752-0509-9-S1-S3, Yu, G., Zhu, H., Domeniconi, C., and Liu, J. Front. Genet. doi: 10.1109/JPROC.2015.2487976. 30, 340347. As GO is routinely updated, it serves as the gold standard and main knowledge source in functional genomics. doi: 10.1093/nar/gks1099, Chen, W.-H., Zhao, X.-M., van Noort, V., and Bork, P. (2013). Liu, X., Yu, G., Domeniconi, C., Wang, J., Ren, Y., and Guo, M. (2019). Inform. Park et al. doi: 10.1007/978-3-319-16706-0_9, Cho, H., Berger, B., and Peng, J. 21 Articles, This article is part of the Research Topic, 4. Peng et al. doi: 10.1016/j.patcog.2013.01.012, Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., et al. Methods 10, 221227. Parametric Bayesian priors and better choice of negative examples improve protein function prediction. Yu et al. PLoS Comput. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). To reduce noise, it applied SVD on the two matrices to compress them into two low-dimensional matrices. Predicting gene function from gene expressions and ontologies,? 46, 20552065. BMC Bioinformatics 9:327. doi: 10.1186/1471-2105-9-327, Mitrofanova, A., Pavlovic, V., and Mishra, B. 88, 209241. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Kulmanov et al. The results obtained in the history to recent evaluation are generally better than those obtained by the dataset partition evaluation. Many models use the hierarchical inter-relations between GO terms and prove that the appropriate use of inter-relations can improve the gene function prediction (Tao et al., 2007; Done et al., 2010; Yu et al., 2015b). Extensive complementarity between gene function prediction methods. doi: 10.1093/bioinformatics/btt160, The Gene Ontology Consortium (2017). Nat. Yu, G., Rangwala, H., Domeniconi, C., Zhang, G., and Zhang, Z. Biol. 9:S2. (2019). (2019). The rest of this review is organized as follows. Each GO term can be modeled as a semantic label and, thus, the gene function prediction task can be treated as a classification problem to determine whether the label is positive for the gene or not. In DeepGO, the deep learning model predicted the GO annotations of genes based on gene sequences and dependencies between GO terms. Biol. 34(Suppl. JW, JC, and XZ participated in the discussion and revision of this manuscript. After that, SimNet applied these weights to fuse the networks into a composite network, and then performed random walks on the composite network to make a prediction. Metrics for GO based protein semantic similarity: a systematic evaluation. 8, 163167. doi: 10.1007/978-1-4939-3743-1, Done, B., Khatri, P., Done, A., and Draghici, S. (2010). (2002). Sci. Methods Mol. Annotating the functional properties of gene products, i.e., RNAs and proteins, is a fundamental task in biology. For example, Yu et al. Mostafavi and Morris (2009) and Cesa-Bianchi et al. (2013) demonstrated that comparing the sequences of just two genes participating in the same biological processes is somewhat inaccurate. Among them, BMA provides a good balance between the maximum and average measure, since the latter two measures are inherently influenced by the number of terms being combined (Pesquita et al., 2009). Brief. Inform. Disease ontology: a backbone for disease semantic integration. in International Conference on Database Systems for Advanced Applications, 313329. Acad. For a gene without any GO annotations, its semantic similarity with other genes is zero. However, the matrix factorization-based methods above lack interpretability of the compressed labels, and suffer from an inherent problem of thresholding both the relevant and irrelevant GO annotations from the predicted numeric gene-term association matrix. Another limitation of semantic similarity-based solutions is that they cannot predict new annotations for a gene without any annotations. Gostruct 2.0: Automated protein function prediction for annotated proteins? in Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (Boston, MA), 6066. Oncotarget 8:60429. doi: 10.18632/oncotarget.19588, Yu, G., Fu, G., Wang, J., and Guo, M. (2017b). Nat. A kernel method for multi-labelled classification? in Advances in Neural Information Processing Systems (Vancouver, BC), 681687. However, most solutions based on semantic similarity are still impacted by incomplete GO annotations. 25, 33893402. (2017) developed a deep learning-based method (DeepGO) to predict gene function from sequences. Predicting protein function using multiple kernels. (2011). Hashing with graphs? in Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA), 18. IEEE 104, 3457. Computational Approaches for Protein Function Prediction: A Survey. To take advantage of information about features of genes and the available-but-scanty negative examples, Fu et al. The funcat, a functional annotation scheme for systematic classification of proteins from whole genomes. According to this rule, a positive prediction for a term but a negative prediction for its ancestor terms with respect to the same gene are inconsistent predictions. To address the last issue, some efforts have been made toward compressing these terms before measuring the semantic similarity (Done et al., 2010; Yu et al., 2017e, 2020b; Zhao et al., 2019a); these were reviewed in previous subsections. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 36, W358?W363. Res. An information-theoretic definition of similarity? in Proceedings of 15th International Conference on Machine Learning (Madison, WI), 296304. U.S.A. 101, 28882893. This problem is also found in multi-label learning (Pillai et al., 2013). (2002). doi: 10.1016/j.compbiolchem.2017.09.010, Yu, G., Zhu, H., and Domeniconi, C. (2015b). Then, HPHash used the hash functions to compress a high-dimensional gene-term association matrix into a low-dimensional binary matrix, and predicted the gene functions therein. doi: 10.1007/978-3-319-41279-5_7, Tao, Y., Li, J., Friedman, C., and Lussier, Y. A survey of computational intelligence techniques in protein function prediction. Natl. 45, D331?D338. To facilitate effective exploration of these semantic measures, some online tools or packages have been developed for the community. (2011). ProSNet: Integrating homology with molecular networks for protein function prediction? in Pacific Symposium on Biocomputing (Hawaii), 2738. However, our current knowledge about the functional taxonomy of gene products is still immature. Figure 1. A. Phylogenomics: improving functional predictions for uncharacterized genes by evolutionary analysis. Natl. (2006) organized the predictions obtained from multiple binary classifiers for different terms in a Bayesian network derived from the GO hierarchy. (2019). in Pacific Symposium on Biocomputing (Hawaii: World Scientific), 299310. doi: 10.1093/nar/gkn276, Zhou, N., Jiang, Y., Bergquist, T. R., Lee, A. J., Kacsoh, B. 41, D983?D986. 18, 12571261. 25, 2529. 48, 10351050. doi: 10.1371/journal.pcbi.1003343, Blei, D. M., Ng, A. Y., and Jordan, M. I. Some efforts have been made toward applying matrix factorization-based solutions to compress sparse GO terms and to infer annotations of genes (Done et al., 2010; Wang et al., 2015; Yu et al., 2017b). Each positive annotation relates a gene with a GO term, and indicates the gene product carries out the function described by this term. Nat. Bioinformatics 29, 14241432. AvgPrecision evaluates the average fraction of GO terms ranked above a particular GO term. King et al. Gene ontology semantic similarity tools: survey on features and challenges for biological knowledge discovery. Such methods ignored the correlations between the GO terms and the imbalanced characteristics of terms; therefore, their accuracy was low. doi: 10.1093/nar/gkj118, Li, H.-D., Menon, R., Omenn, G. S., and Guan, Y. As the need of human knowledge (i.e., GO and its annotations) for artificial intelligence in biology increases, we believe the study of GO for gene function prediction and for other biomedical data mining tasks will be fast growing. Eisen (1998) found that utilizing evolutionary information improved gene function prediction. Emmert-Streib, F., and Dehmer, M. (2009). Biol. doi: 10.1016/j.cbpa.2006.11.039, Thomas, P. D., Wood, V., Mungall, C. J., Lewis, S. E., and Blake, J. (2007). doi: 10.1109/TITB.2009.2033116. Knowl. Fifth, most existing solutions focus on predicting the new annotations of a newly-sequenced gene or the missing annotations of a gene with sparse annotations. Genome Res. (2015). NtN (Done et al., 2010) and IFDR (Yu et al., 2017b) are methods already mentioned in section 3.1.2. Guided by this observation, some databases based on the phylogenetic trees of animal-gene families appeared, such as TreeFam (Li et al., 2006). Genome Res. Isoform function prediction based on bi-random walks on a heterogeneous network. Comput. YZ and GY drafted the manuscript. Bioinformatics 32, 36453653. Furthermore, semantic measures are computed with respect to massive GO terms and, thus, are less reliable with sparse annotations. doi: 10.1073/pnas.0307326101, King, O. D., Foulger, R. E., Dwight, S. S., White, J. V., and Roth, F. P. (2003). doi: 10.1142/9789812704856_0029, Lee, D. D., and Seung, H. S. (1999). Obviously, these solutions have some overlaps with the ones introduced in the previous subsections. Three issues in gene function prediction (left), and categorization of existing computational solutions based on GO (right). Researchers also recently employed hashing learning techniques to convert the typical one-hot coding of massive GO terms into short binary hashing codes. Nat. 1446,368. doi: 10.1093/bioinformatics/17.8.721, Huntley, R. P., Sawford, T., Martin, M. J., and Donovan, C. (2014). The impact of incomplete knowledge on the evaluation of protein function prediction: a structured-output learning perspective. 9:e1003343. Nat. Bioinformatics 30, i609?i616. Kernel-based data fusion and its application to protein function prediction in yeast? in Pacific Symposium on Biocomputing (Hawaii: World Scientific), 300311. By December 2019, GO included more than 45,000 terms, and each gene was only annotated with several or dozens of these terms. Figure 3. Then, it restored the predictions to the original GO terms. doi: 10.1038/75556, PubMed Abstract | CrossRef Full Text | Google Scholar, Barabsi, A.-L., Gulbahce, N., and Loscalzo, J. To solve this problem, Yu et al. (2016b) studied cross-species gene function prediction based on semantic similarity. A bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Comput. (2012b) proposed a gene function prediction model based on weak label learning (ProWL), in which the labels of the annotated training data were incomplete. Gene function prediction methods mainly utilize the structure of GO and biological features (including nucleotide/amino acids sequences, gene expression, and interaction data, etc.) These generally obtained an improved accuracy (Mostafavi et al., 2008; Mostafavi and Morris, 2010; Yu et al., 2012a, 2015a). doi: 10.1093/bib/bbw067, Mi, H., Muruganujan, A., Casagrande, J. T., and Thomas, P. D. (2013). MacroAvgF1 averages the F1 scores of different GO terms, and is more affected by the performance of sparse GO terms with fewer relevant genes. (2019) recently experimentally evaluated a series of label-compression solutions based on matrix factorization and proved that compressed labels can boost the prediction performance. Identifying noisy functional annotations of proteins using sparse semantic similarity. doi: 10.1186/2047-217X-3-4. A large-scale evaluation of computational protein function prediction. Ten quick tips for using the gene ontology. (2018) presented a similarity measure that integrated information from gene co-function networks, the GO structure and annotations. For example, GO:0048087 is a direct child and also a grandson of GO:0048066, and its furthest distance to the root term is 5, while GO:0006856 is another direct child of GO:0048066 and its furthest distance to the root is 4, so GO:0006856 is plotted at a higher level than GO:0048087. Trends Genet. 12, 203214. 9:e1003073. Comput. Wang et al. 11, 411. Therefore, it is interesting to leverage the shared GO structure and complementary annotations of genes for cross-species gene function prediction. For example, You et al. (2012). Biol. 53, 17531765. Biol. In the early stages, typical cross-species solutions only involved the sequence data along with BLAST and PSI-BLAST (Zhang et al., 1997), but these solutions were unreliable, and the sequence identification was <25% (Shehu et al., 2016). Existing methods of computational gene function prediction generally focus on the three tasks ( illustrated in Figure 4): (i) predicting missing (new) annotations, which updates some entries in Y with value 0 into 1 to identify new functional annotations of genes; (ii) identifying noisy annotations, which updates some entries in Y with value 1 into 1 to remove these false positive annotations; (iii) predicting negative examples, which updates some entries in Y with value 0 into 1 to state that the gene clearly does not carry out this function. Some studies show that the GO annotations of homologous genes across species are complementary. Biol. doi: 10.1093/bioinformatics/btq262, Mostafavi, S., Ray, D., Wardefarley, D., Grouios, C., and Morris, Q. doi: 10.1371/journal.pcbi.1000443, Pillai, I., Fumera, G., and Roli, F. (2013). Biol. doi: 10.1360/N112017-00105, Lu, C., Wang, J., Zhang, Z., Yang, P., and Yu, G. (2016). (2013a) presented an algorithm called ProDM, which uses the maximized dependency between the features and GO annotations of genes to predict missing (or new) GO annotations of genes.

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