Based on a similar concept of combining primary protein structure and PPI, GONET (Li et al., 2020), a novel model, was built by employing CNN, RNN, and Attention layer for human and mouse sequences. Online tool available at, Deep network fusion, captures high-level features of several network data for AFP. AE is a neural network for data transformation. This type of For These features are fed to the k-NN and logistic regression models to produce the functions of no-knowledge proteins on a large scale. Next, topological features of PPI are obtained by the Deepwalk algorithm. Recurrent neural network (RNN) is a deep learning architecture developed especially for sequential data. Dataset used in this comparison (final benchmark CAFA3) is available at Figshare: Zhou, Naihui (2019): Supplementary_data. The user can implement their self-configured models in a downloadable program or run two pre-trained models (Szalkai & Grolmusz, 2018a) on a web server. Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the Nomenclature and Classification of Enzymes, Deeptext2go: improving large-scale protein function prediction with deep semantic text representation, Netgo: improving large-scale protein function prediction with massive network information, Golabeler: improving sequence-based large-scale protein function prediction by learning to rank, Interproscanan integration platform for the signature-recognition methods in interpro, Ontoblast function: from sequence similarities directly to potential functional annotations by ontology terms, Deepfunc: a deep learning framework for accurate prediction of protein functions from protein sequences and interactions, A deep learning framework for gene ontology annotation with sequence-and network-based information, The cafa challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens, A literature review of gene function prediction by modeling gene ontology, Protein function prediction using deep restricted boltzmann machines, Web server, which is a part of the Ontologies TO GenomeMatrix tool (. Variant of the MRF-based approach on PPI that infers novel predictions for unannotated proteins. progress in the field that systematically reviews the most exciting advances in scientific literature. Functional annotation system based on PSI-BLAST hits, exploiting matched sequences up to an unconventionally high threshold, and mining highly relevant GO terms from UniProt to score functions for unknown proteins. ProLanGo (Cao et al., 2017) is the first tool that applies Neural Machine Translation (NMT) developed by Google in AFP. The Feature Paper can be either an original research article, a substantial novel research study that often involves and will receive updates in the daily or weekly email digests if turned on. NetGO improves the performance of a large scale AFP by accessing the enormous proteinprotein network of over 2000 species in the STRING database (Szklarczyk et al., 2016). Typos, corrections needed, missing information, abuse, etc. Branch (a) of Fig. Deep learning models are inspired by neural networks in the human brain. Visit our dedicated information section to learn more about MDPI. In another study, deepNF (Gligorijevi, Barot & Bonneau, 2018) was constructed by multimodal deep AE to capture hidden information in proteins from different types of interaction networks. Machine learning-based tools have been developed to identify the hidden relationships between various protein features (sequence, structure, or other related evolutionary evidence) and functional labels, based on a training set (a group of fully characterized macromolecules), and use that information to generate annotations for novel proteins. Finally, successful solutions for GO term prediction can be expanded to include other functional resources (EC, pathways, etc. Protein function prediction is a crucial part of genome annotation. Accordingly, the critical assessment of functional annotation (CAFA) (Radivojac et al., 2013; Jiang et al., 2016; Zhou et al., 2019) is a community-based experiment that provides a large-scale evaluation of computational protein function prediction methods in a time-delayed manner. Herein, we reviewed automated protein function prediction using GO terms (ranging from traditional solutions to the most recently developed deep learning-based tools). In order to be human-readable, please install an RSS reader. DeepAdd (Du et al., 2020) was inspired by DeepGO server and provides a solution for AFP, utilizing a CNN framework to learn vector representations from sequences and additional information. Together with the constant expansion of -omics databases, data-driven methods have shown excellent applications in various areas, with a promising trend in functional annotation. Meanwhile,Zhao et al. This model was utilized for converting input vectors from InterPro and predicting GO terms (Zhang et al., 2019). Bonetta & Valentino (2020) demonstrate protein function prediction in the machine learning workflow. Author to whom correspondence should be addressed. It can be used to infer many types of information, such as is-a or part-of. Integrates comprehensive sequence and PPI information for AFP. permission is required to reuse all or part of the article published by MDPI, including figures and tables. Meanwhile, deep learning, a fast-evolving discipline in data-driven approach, exhibits impressive potential with respect to assigning GO terms to amino acid sequences. Similar to DeepGOPlus, TALE is also combined with sequence similarity as TALE+ model to enhance its performance. As is shown in Fig. interesting to authors, or important in this field. Initially, functional annotations were assigned to uncharacterized proteins based on a simple principle: a protein sequence was searched in databases of experimentally curated proteins and if there were any similar proteins retrieved (similar in a specifically defined way), GO terms associated with the retrieved sequences were assigned to the query protein. In addition to the alignment-based methods, several other predictors are available that transfer function annotations based on the similarity at the level of protein structure, protein family, or phylogenomics (Shehu, Barbar & Molloy, 2016). The two latest reviews are prepared byBonetta & Valentino (2020) andZhao et al. Our promise Using the k-nearest neighbor (k-NN) algorithm, PANNZER2 (Trnen, Medlar & Holm, 2018) provides a fast functional annotation systems based on sequence homology and other annotation predictors. 2) and partial mode (Fig. The results of the analysis are shown in terms of GO domains and types of sequences (NK vs. All) in full mode (Fig. Common use cases Multiple requests from the same IP address are counted as one view. We presented not only an overview of the literature, but also, a performance comparison of the emerging solutions. This allows the flexible annotation of proteins with respect to the various levels of functionfrom general to specific termsdepending on the available evidence(Stein, 2001). Typically, unsupervised learning models are employed for clustering, reducing dimensions, and transforming data. fame mirna prediction enrichment functional micrornas assignment via target These updates will appear in your home dashboard each time you visit PeerJ. The output form of the two methods was similar; however, the latter performed better on six different datasets. The functional assignment of amino acid sequences from 3D structures was proposed by (Tavanaei et al., 2016). Makrodimitris, S.; van Ham, R.C.H.J. Based on previous studies(Jung & Thon, 2006; Jung et al., 2010) and the aforementioned surveys, our review is divided in two main parts offering an overview of the field (from its inception to its present state). Nonetheless, this approach is expensive and laborious, and thus, it is difficult to scale. Automatic Gene Function Prediction in the 2020s. DeepText2GO (You, Huang & Zhu, 2018a) is a consensus approach, integrating deep semantic text representation from MEDLINE citations (ncb, 2018) as text information, and sequence information obtained via BLAST and InterProScan. The authors converted amino acid sequences and GO terms into ProLan and GOLan languages, respectively. https://doi.org/10.3390/genes11111264, Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. With respect to GO-based functional annotation, DNNs have been employed as multi-task DNNs (MTDNNs) (Fa et al., 2018) or a series of MTDNNs (Rifaioglu et al., 2019). Jaehee Jung conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. Finally, we discussed the remaining major challenges in the field, and emphasized the future directions for protein function prediction with GO. genes thaliana arabidopsis comparative genomic analyses phenomics mining insight rsc molecular pubs The probability of assigning a term to the query sequence is determined by the functionally discriminating residues (FDRs), a position-specific scoring matrix (PSSM) for the FDRs, and a score-to-probability table prepared using training sequences. These neural networks are employed to process protein sequences as a natural language (Cao et al., 2017) or are combined with the CNN model (Zhang et al., 2020) to provide GO annotations. In that part, we summarize three main sub-categories of the traditional approach, and mention prominent or most recent corresponding studies. root msystems elucidating microbiome biosynthetic peptide nonribosomal In terms of evaluation, two modes were designed. Depending on the available sources, they can be based on integrated data or sequence information only, considering a hybrid approach. Therefore, our paper suggests the possibility of predicting protein function using both conventional learning and deep learning, further indicating that better predictive performance can be expected by comparing several methods with each other. In another study, artificial neural networks proposed by Szalkai & Grolmusz (2018a) utilized six CNN layers of increasing depth. NetGO (You et al., 2019) is an extension of GoLabeler (You et al., 2018b), which employs the learning-to-rank(LTR) model to integrate sequence-based evidence. 2020. The excellent potential of deep learningthe latest discipline of machine learninghas been demonstrated in several application fields, including bioinformatics (Min, Lee & Yoon, 2017; Li et al., 2019). Generally, protein function identification is accomplished through manual or computational annotation. Source code and data available at, An implementation of two stacked multi-layer structures to predict GO terms from the sequence and structure properties. Feature Papers represent the most advanced research with significant potential for high impact in the field. Many studies have discussed protein functional annotation from different perspectives. Additionally, DEEPred filtered some false positives at the post-processing step, thus increasing their precision. Currently, Gene Ontology (GO)(Ashburner et al., 2000; Consortium, 2015) is the most comprehensive resource, as it possesses all the desirable properties of a functional classification system(Pandey, Kumar & Steinbach, 2006). In that suggestion, feature representations are extracted to train the final SVM classifier to produce GO terms for human proteins. All articles published by MDPI are made immediately available worldwide under an open access license. Another variant solution based on sequence alignment is INGA (Piovesan et al., 2015), in which proteinprotein interaction (PPI) network data is combined with domain assignment and sequence similarity from BLAST, to attain a consensus prediction of GO functions using enrichment analysis. For the CC class, two baselines (Naive and BLAST) were at the top for Fmax and recall, followed by FFPred3. permission provided that the original article is clearly cited. https://doi.org/10.3390/genes11111264, Makrodimitris, Stavros, Roeland C. H. J. van Ham, and Marcel J. T. Reinders. Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach. RNNs have a DNN backbone, and the units of the hidden layer are connected. Another tool, Prediction of Gene Ontology terms (PoGO) (Jung et al., 2010), has been developed from Automatic Annotation of Protein Functional Class (AAPFC) (Jung & Thon, 2006). Prediction methods have recently witnessed rapid development, owing to the emergence of high-throughput sequencing technologies. Then, a retrieved sequence with the highest alignment scoreas per a predetermined thresholdis identified and its annotation is transferred to the query protein. The GO consortium created a database for a controlled vocabulary describing the functional properties of genomic products (e.g.,genes, proteins, and RNA). 
Produces output as a clickable graph in four steps, including homologous sequence search, minimum cover graph construction, and assigning ontologies after scoring them. mirnas necrotizing differentially expressed rats enterocolitis rna sequencing reveals Bi-directional LSTM (Bi-LSTM) is an LSTM model that processes data in two directions, forwards and backwards. The authors compared two annotation solutions, i.e., tSVD, presented in Pinoli, Chicco & Masseroli (2013), and the AE neural network. On the other hand, FFPred-GAN (Wan & Jones, 2020) utilizes GAN for feature enrichment to feed conventional machine learning models, especially SVM. However, it has since become an effective architecture not only for multi-dimensional data, but also for one-dimensional input, such as sentences or genomic sequences. articles published under an open access Creative Common CC BY license, any part of the article may be reused without protein structure poster folding prediction function posters functions relationships between examples You can also choose to receive updates via daily or weekly email digests. Input data are processed in a unidirectional manner along the layers, from the first to the final stage. "Following" is like subscribing to any updates related to a publication. The research direction is currently shifting toward this new approach, with even more efficient solutions being developed. The first parts covers the conventional approach and includes solutions that do not use deep learning, while the second part describes methods that rely on deep learning to address the problem of protein functional annotation. Further, part-of implies that the child node is necessarily part of the parent. There are two sequence types in the benchmark dataset, NK (no-knowledge) and LK (limited-knowledge), which have been coined in CAFA2 (Jiang et al., 2016). 2, deep learning-based models (DEEPred and DeepGOPlus) showed a competitive performance compared to conventional models in terms of MF and BP prediction; in particular, DEEPred acquired the highest precision in the three GO categories. (2020). Iranian journal of pharmaceutical research: IJPR, Proteins: Structure, Function, and Bioinformatics, Frontiers in Bioengineering and Biotechnology, Department of Computer Science and Engineering, University of Minnesota, IEEE/ACM Transactions on Computational Biology and Bioinformatics, http://bioinf.cs.ucl.ac.uk/downloads/mtdnn, https://github.com/duongvtt96/Comparison-GO-annotation-systems, https://doi.org/10.6084/m9.figshare.8135393.v3, Database resources of the national center for biotechnology information, Gapped blast and psi-blast: a new generation of protein database search programs, Proteomics applications in health: biomarker and drug discovery and food industry, Gene ontology: tool for the unification of biology, Autoencoders, unsupervised learning, and deep architectures, Proceedings of ICML workshop on unsupervised and transfer learning, Machine learning techniques for protein function prediction, Sdn2go: an integrated deep learning model for protein function prediction, Prolango: protein function prediction using neural machine translation based on a recurrent neural network, TALE: transformer-based protein function Annotation with joint sequenceLabel Embedding, Deep autoencoder neural networks for gene ontology annotation predictions, Proceedings of the 5th ACM conference on bioinformatics, computational biology, and health informatics, Computational methods for annotation transfers from sequence, Ffpred 3: feature-based function prediction for all gene ontology domains, An integrated probabilistic model for functional prediction of proteins, Mapping gene ontology to proteins based on proteinprotein interaction data, Deepadd: protein function prediction from k-mer embedding and additional features, Predicting human protein function with multi-task deep neural networks, A decision-theoretic generalization of on-line learning and an application to boosting, Automated protein function predictionthe genomic challenge, deepnf: deep network fusion for protein function prediction, Gofdr: a sequence alignment based method for predicting protein functions, Pfp: automated prediction of gene ontology functional annotations with confidence scores using protein sequence data, Automated gene ontology annotation for anonymous sequence data, The genomematrix information retrieval system, Poster Abstracts of HGM2002 Human Genome Meeting (HGM2002), An expanded evaluation of protein function prediction methods shows an improvement in accuracy, Automatic annotation of protein functional class from sparse and imbalanced data sets, VLDB workshop on data mining and bioinformatics, Pogo: prediction of gene ontology terms for fungal proteins, The kegg resource for deciphering the genome, Gofigure: automated gene ontology annotation, Bayesian markov random field analysis for protein function prediction based on network data, Deepgoplus: improved protein function prediction from sequence, Deepgo: predicting protein functions from sequence and interactions using a deep ontology-aware classifier, Ms-k nn: protein function prediction by integrating multiple data sources, Handwritten digit recognition with a back-propagation network, Advances in neural information processing systems, Predicting protein function from protein/protein interaction data: a probabilistic approach, Deep learning in bioinformatics: introduction, application, and perspective in the big data era, Gonet: a deep network to annotate proteins via recurrent convolution networks, 2020 IEEE international conference on bioinformatics and biomedicine (BIBM), volume 2, Ffpred: an integrated feature-based function prediction server for vertebrate proteomes, Inferring function using patterns of native disorder in proteins, A combined algorithm for genome-wide prediction of protein function, Gotcha: a new method for prediction of protein function assessed by the annotation of seven genomes, Probabilistic protein function prediction from heterogeneous genome-wide data, Beyond homology transfer: deep learning for automated annotation of proteins, Computational approaches for protein function prediction: a survey, Integrating multi-network topology for gene function prediction using deep neural networks, Improved biomolecular annotation prediction through weighting scheme methods, International meeting on computational intelligence methods for bioinformatics and biostatistics, Tenth edition, CIBB 2013, Computational algorithms to predict gene ontology annotations, Inga: protein function prediction combining interaction networks, domain assignments and sequence similarity, A large-scale evaluation of computational protein function prediction, Protein function predictionthe power of multiplicity, Deepred: automated protein function prediction with multi-task feed-forward deep neural networks, The funcat, a functional annotation scheme for systematic classification of proteins from whole genomes, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, Functional annotation prediction: all for one and one for all, Pfp-wgan: protein function prediction by discovering gene ontology term correlations with generative adversarial networks, Network-based prediction of protein function, A survey of computational methods for protein function prediction, An overview of in silico protein function prediction, Hierachial protein function prediction with tails-gnns, Genome annotation: from sequence to biology, Near perfect protein multi-label classification with deep neural networks, Seclaf: a webserver and deep neural network design tool for hierarchical biological sequence classification, The string database in 2017: quality-controlled proteinprotein association networks, made broadly accessible, Towards recognition of protein function based on its structure using deep convolutional networks, 2016 IEEE international conference on bioinformatics and biomedicine (BIBM), Pannzer2: a rapid functional annotation web server, Gopet: a tool for automated predictions of gene ontology terms, Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks, Predicting protein function from sequence and structural data, Enzyme nomenclature 1992. Researchers are employing various approaches to efficiently predict the GO terms. 
We used Google Scholar https://scholar.google.com/ as the literature database to retrieve relevant publications, without applying any restrictions as to the publishing data, journal, or publisher. The area under the precisionrecall curve (AUPR), which is a measurement of highly imbalanced data, was also considered. Meanwhile, PFP-WGAN (Seyyedsalehi et al., 2021) is one of two latest ideas that use GAN to infer the functionalities of proteins. They play a role in numerous processes, including biochemical reactions, transmission of signals, nutrient transport, immune system boosting, etc. For example, the negative protein set for each GO term is four times as large as the positive one, this leads to low false positive and high false negative predictions. Subsequently, SVM and a linear classifier (Freund & Schapire, 1997) are used as base-level classifiers before the meta-learning step. Genes 2020, 11, 1264. Here, the solution consisted of layers shared by all tasks (GO labels), which are stacked in parallel with task-specific layers. Protein GO annotations are inferred based on a hierarchically structured classifier. Finally, the method concatenates two types of features (sequence- and network-based input) to fit an FCDN. Because of the variability in the vocabulary used to define protein function, which makes the annotation process confusing to both humans and machines, various databases have been proposed to provide a standardized scheme, such as the Enzyme Commission (EC)(Webb, 1992), Functional Catalogue (FunCat)(Ruepp et al., 2004), and Kyoto Encyclopedia of Genes and Genomes (KEGG)(Kanehisa et al., 2004).
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