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Payra AK, Saha B, Ghosh A. MEM-FET: Essential protein prediction using membership feature and machine learning approach. Proteins 2024; 92:60-75. [PMID: 37638618 DOI: 10.1002/prot.26577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 02/21/2023] [Accepted: 08/08/2023] [Indexed: 08/29/2023]
Abstract
Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day-to-day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM-FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM-FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM-FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.
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Affiliation(s)
- Anjan Kumar Payra
- Department of Computer Science and Engineering, Dr. Sudhir Chandra Sur Degree Engineering College, Kolkata, India
| | - Banani Saha
- Department of Computer Science and Engineering, University of Calcutta, Kolkata, India
| | - Anupam Ghosh
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India
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2
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Tian Z, Fang H, Teng Z, Ye Y. GOGCN: Graph Convolutional Network on Gene Ontology for Functional Similarity Analysis of Genes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1053-1064. [PMID: 35687647 DOI: 10.1109/tcbb.2022.3181300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The measurement of gene functional similarity plays a critical role in numerous biological applications, such as gene clustering, the construction of gene similarity networks. However, most existing approaches still rely heavily on traditional computational strategies, which are not guaranteed to achieve satisfactory performance. In this study, we propose a novel computational approach called GOGCN to measure gene functional similarity by modeling the Gene Ontology (GO) through Graph Convolutional Network (GCN). GOGCN is a graph-based approach that performs sufficient representation learning for terms and relations in the GO graph. First, GOGCN employs the GCN-based knowledge graph embedding (KGE) model to learn vector representations (i.e., embeddings) for all entities (i.e., terms). Second, GOGCN calculates the semantic similarity between two terms based on their corresponding vector representations. Finally, GOGCN estimates gene functional similarity by making use of the pair-wise strategy. During the representation learning period, GOGCN promotes semantic interaction between terms through GCN, thereby capturing the rich structural information of the GO graph. Further experimental results on various datasets suggest that GOGCN is superior to the other state-of-the-art approaches, which shows its reliability and effectiveness.
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Payra AK, Saha B, Ghosh A. MM-CCNB: Essential protein prediction using MAX-MIN strategies and compartment of common neighboring approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 228:107247. [PMID: 36427433 DOI: 10.1016/j.cmpb.2022.107247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 10/16/2022] [Accepted: 11/14/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Proteins are indispensable for the flow of the life of living organisms. Protein pairs in interaction exhibit more functional activities than individuals. These activities have been considered an essential measure in predicting their essentiality. Neighborhood approaches have been used frequently in the prediction of essentiality scores. All paired neighbors of the essential proteins are nominated for the suitable candidate seeds for prediction. Still now Jaccard's coefficient is limited to predicting functions, homologous groups, sequence analysis, etc. It really motivate us to predict essential proteins efficiently using different computational approaches. METHODS In our work, we proposed modified Jaccard's coefficient to predict essential proteins. We have proposed a novel methodology for predicting essential proteins using MAX-MIN strategies and modified Jaccard's coefficient approach. RESULTS The performance of our proposed methodology has been analyzed for Saccharomyces cerevisiae datasets with an accuracy of more than 80%. It has been observed that the proposed algorithm is outperforms with an accuracy of 0.78, 0.74, 0.79, and 0.862 for YDIP, YMIPS, YHQ, and YMBD datasets respectivly. CONCLUSIONS There are several computational approaches in the existing state-of-art model of essential protein prediction. It has been noted that our predicted methodology outperforms other existing models viz. different centralities, local interaction density combined with protein complexes, modified monkey algorithm and ortho_sim_loc methods.
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Affiliation(s)
- Anjan Kumar Payra
- Department of Computer Science & Engineering, Dr. Sudhir Chandra Sur Degree Engineering College, 540, Dum Dum Road, Near Dum Dum Jn. Station, Surermath, Kolkata 700074, India.
| | - Banani Saha
- Department of Computer Science & Engineering, University of Calcutta, Saltlake City Kolkata 700073, India
| | - Anupam Ghosh
- Department of Computer Science & Engineering, Netaji Subhash Engineering College, Techno City, Panchpota, Garia, Kolkata 700152, India.
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Boundary equilibrium SR: effective loss functions for single image super-resolution. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04162-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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5
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Hotness prediction of scientific topics based on a bibliographic knowledge graph. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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6
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Zhang J, Zhu M, Qian Y. protein2vec: Predicting Protein-Protein Interactions Based on LSTM. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1257-1266. [PMID: 32750870 DOI: 10.1109/tcbb.2020.3003941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The semantic similarity of gene ontology (GO) terms is widely used to predict protein-protein interactions (PPIs). The traditional semantic similarity measures are based mainly on manually crafted features, which may ignore some important hidden information of the gene ontology. Moreover, those methods usually obtain the similarity between proteins from similarity between GO terms by some simple statistical rules, such as MAX and BMA (best-match average), oversimplifying the possible complex relationship between the proteins and the GO terms annotated with them. To overcome the two deficiencies, we propose a new method named protein2vec, which characterizes a protein with a vector based on the GO terms annotated to it and combines the information of both the GO and known PPIs. We firstly try to apply the network embedding algorithm on the GO network to generate feature vectors for each GO term. Then, Long Short-Time Memory (LSTM) encodes the feature vectors of the GO terms annotated with a protein into another vector (called protein vector). Finally, two protein vectors are forwarded into a feedforward neural network to predict the interaction between the two corresponding proteins. The experimental results show that protein2vec outperforms almost all commonly used traditional semantic similarity methods.
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Ieremie I, Ewing RM, Niranjan M. TransformerGO: predicting protein-protein interactions by modelling the attention between sets of gene ontology terms. Bioinformatics 2022; 38:2269-2277. [PMID: 35176146 PMCID: PMC9363134 DOI: 10.1093/bioinformatics/btac104] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/26/2022] [Accepted: 02/15/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Protein-protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations, such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms. RESULTS We propose TransformerGO, a model that is capable of capturing the semantic similarity between GO sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine-learning-based approaches on large datasets from Saccharomyces cerevisiae and Homo sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions. AVAILABILITY AND IMPLEMENTATION https://github.com/Ieremie/TransformerGO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Rob M Ewing
- Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Mahesan Niranjan
- Vision, Learning & Control Group, University of Southampton, Southampton SO17 1BJ, UK
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Edera AA, Milone DH, Stegmayer G. Anc2vec: embedding gene ontology terms by preserving ancestors relationships. Brief Bioinform 2022; 23:6523148. [PMID: 35136916 DOI: 10.1093/bib/bbac003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/13/2021] [Accepted: 01/04/2022] [Indexed: 12/11/2022] Open
Abstract
The gene ontology (GO) provides a hierarchical structure with a controlled vocabulary composed of terms describing functions and localization of gene products. Recent works propose vector representations, also known as embeddings, of GO terms that capture meaningful information about them. Significant performance improvements have been observed when these representations are used on diverse downstream tasks, such as the measurement of semantic similarity between GO terms and functional similarity between proteins. Despite the success shown by these approaches, existing embeddings of GO terms still fail to capture crucial structural features of the GO. Here, we present anc2vec, a novel protocol based on neural networks for constructing vector representations of GO terms by preserving three important ontological features: its ontological uniqueness, ancestors hierarchy and sub-ontology membership. The advantages of using anc2vec are demonstrated by systematic experiments on diverse tasks: visualization, sub-ontology prediction, inference of structurally related terms, retrieval of terms from aggregated embeddings, and prediction of protein-protein interactions. In these tasks, experimental results show that the performance of anc2vec representations is better than those of recent approaches. This demonstrates that higher performances on diverse tasks can be achieved by embeddings when the structure of the GO is better represented. Full source code and data are available at https://github.com/sinc-lab/anc2vec.
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Affiliation(s)
- Alejandro A Edera
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
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Acharya S, Cui L, Pan Y. A Refined 3-in-1 Fused Protein Similarity Measure: Application in Threshold-Free Hub Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:192-206. [PMID: 32070994 DOI: 10.1109/tcbb.2020.2973563] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
An exhaustive literature survey shows that finding protein/gene similarity is an important step towards solving widespread bioinformatics problems, such as predicting protein-protein interactions, analyzing Protein-Protein Interaction Networks (PPINs), gene prioritization, and disease gene/protein detection. In this article, we have proposed an improved 3-in-1 fused protein similarity measure called FuSim-II. It is built upon combining the weighted average of biological knowledge extracted from three potential genomic/ proteomic resources such as Gene Ontology (GO), PPIN, and protein sequence. Furthermore, we have shown the application of the proposed measure in detecting potential hub-proteins from a given PPIN. Aiming that, we have proposed a multi-objective clustering-based protein hub detection framework with FuSim-II working as the underlying proximity measure. The PPINs of H. Sapiens and M. Musculus organisms are chosen for experimental purposes. Unlike most of the existing hub-detection methods, the proposed technique does not require to follow any protein degree cut-off or threshold to define hubs. A thorough assessment of efficiency between proposed and existing eight protein similarity measures along with eight single/multi-objective clustering methods has been carried out. Internal cluster validity indices like Silhouette and Davies Bouldin (DB) are deployed to accomplish analytical study. Also, a comparative performance analysis between proposed and five existing hub-proteins detection algorithms is conducted through the enrichment of essentiality study. The reported results show the improved performance of FuSim-II over existing protein similarity measures in terms of identifying functionally related proteins as well as relevant hub-proteins. Supplementary material is available at http://csse.szu.edu.cn/staff/cuilz/eng/index.html.
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Cartealy I, Liao L. Predicting metabolic pathway membership with deep neural networks by integrating sequential and ontology information. BMC Genomics 2021; 22:691. [PMID: 34579673 PMCID: PMC8474704 DOI: 10.1186/s12864-021-07629-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Inference of protein's membership in metabolic pathways has become an important task in functional annotation of protein. The membership information can provide valuable context to the basic functional annotation and also aid reconstruction of incomplete pathways. Previous works have shown success of inference by using various similarity measures of gene ontology. RESULTS In this work, we set out to explore integrating ontology and sequential information to further improve the accuracy. Specifically, we developed a neural network model with an architecture tailored to facilitate the integration of features from different sources. Furthermore, we built models that are able to perform predictions from pathway-centric or protein-centric perspectives. We tested the classifiers using 5-fold cross validation for all metabolic pathways reported in KEGG database. CONCLUSIONS The testing results demonstrate that by integrating ontology and sequential information with a tailored architecture our deep neural network method outperforms the existing methods significantly in the pathway-centric mode, and in the protein-centric mode, our method either outperforms or performs comparably with a suite of existing GO term based semantic similarity methods.
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Affiliation(s)
- Imam Cartealy
- University of Delaware, Computer and Information Sciences, 101 Smith Hall, Newark, 19716, DE, US
| | - Li Liao
- University of Delaware, Computer and Information Sciences, 101 Smith Hall, Newark, 19716, DE, US.
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Martins YC, Ziviani A, Nicolás MF, de Vasconcelos ATR. Large-Scale Protein Interactions Prediction by Multiple Evidence Analysis Associated With an In-Silico Curation Strategy. FRONTIERS IN BIOINFORMATICS 2021; 1:731345. [PMID: 36303787 PMCID: PMC9581021 DOI: 10.3389/fbinf.2021.731345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022] Open
Abstract
Predicting the physical or functional associations through protein-protein interactions (PPIs) represents an integral approach for inferring novel protein functions and discovering new drug targets during repositioning analysis. Recent advances in high-throughput data generation and multi-omics techniques have enabled large-scale PPI predictions, thus promoting several computational methods based on different levels of biological evidence. However, integrating multiple results and strategies to optimize, extract interaction features automatically and scale up the entire PPI prediction process is still challenging. Most procedures do not offer an in-silico validation process to evaluate the predicted PPIs. In this context, this paper presents the PredPrIn scientific workflow that enables PPI prediction based on multiple lines of evidence, including the structure, sequence, and functional annotation categories, by combining boosting and stacking machine learning techniques. We also present a pipeline (PPIVPro) for the validation process based on cellular co-localization filtering and a focused search of PPI evidence on scientific publications. Thus, our combined approach provides means to extensive scale training or prediction of new PPIs and a strategy to evaluate the prediction quality. PredPrIn and PPIVPro are publicly available at https://github.com/YasCoMa/predprin and https://github.com/YasCoMa/ppi_validation_process.
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Affiliation(s)
- Yasmmin Côrtes Martins
- Bioinformatics Laboratory, National Laboratory of Scientific Computing, Petrópolis, Brazil
| | - Artur Ziviani
- Data Extreme Lab (DEXL), National Laboratory of Scientific Computing, Petrópolis, Brazil
| | - Marisa Fabiana Nicolás
- Bioinformatics Laboratory, National Laboratory of Scientific Computing, Petrópolis, Brazil
| | - Ana Tereza Ribeiro de Vasconcelos
- Bioinformatics Laboratory, National Laboratory of Scientific Computing, Petrópolis, Brazil
- *Correspondence: Ana Tereza Ribeiro de Vasconcelos,
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Khan S, Tufail M, Khan MT, Khan ZA, Iqbal J, Alam M. A novel semi-supervised framework for UAV based crop/weed classification. PLoS One 2021; 16:e0251008. [PMID: 33970938 PMCID: PMC8109769 DOI: 10.1371/journal.pone.0251008] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 04/18/2021] [Indexed: 11/19/2022] Open
Abstract
Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control will be a critical component significantly helping in achieving the goal. A prerequisite for such control is a robust classification system that could accurately identify weed crops in a field. In this regard, Unmanned Aerial Vehicles (UAVs) can acquire high-resolution images providing detailed information for the distribution of weeds and offers a cost-efficient solution. Most of the established classification systems deploying UAV imagery are supervised, relying on image labels. However, this is a time-consuming and tedious task. In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth stage. The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. The proposed system was evaluated extensively on the Red Green Blue (RGB) images obtained by a quadcopter in two different croplands (pea and strawberry). The method achieved an average accuracy of 90% when 80% of training data was unlabeled. The proposed system was compared with several standards supervised learning classifiers and the results demonstrated that this technique could be applied for challenging tasks of crops and weeds classification, mainly when the labeled samples are small at less training time.
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Affiliation(s)
- Shahbaz Khan
- Department of Mechatronics Engineering, University of Engineering & Technology, Peshawar, Pakistan
- Advanced Robotics and Automation Laboratory, National Center of Robotics and Automation (NCRA), Rawalpindi, Pakistan
| | - Muhammad Tufail
- Department of Mechatronics Engineering, University of Engineering & Technology, Peshawar, Pakistan
- Advanced Robotics and Automation Laboratory, National Center of Robotics and Automation (NCRA), Rawalpindi, Pakistan
| | - Muhammad Tahir Khan
- Department of Mechatronics Engineering, University of Engineering & Technology, Peshawar, Pakistan
- Advanced Robotics and Automation Laboratory, National Center of Robotics and Automation (NCRA), Rawalpindi, Pakistan
| | - Zubair Ahmad Khan
- Department of Mechatronics Engineering, University of Engineering & Technology, Peshawar, Pakistan
| | - Javaid Iqbal
- College of Electrical & Mechanical Engineering (CEME) National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Mansoor Alam
- Department of Mechatronics Engineering, University of Engineering & Technology, Peshawar, Pakistan
- Advanced Robotics and Automation Laboratory, National Center of Robotics and Automation (NCRA), Rawalpindi, Pakistan
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Cardoso C, Sousa RT, Köhler S, Pesquita C. A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain. Database (Oxford) 2020; 2020:baaa078. [PMID: 33181823 PMCID: PMC7661097 DOI: 10.1093/database/baaa078] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/13/2020] [Accepted: 08/24/2020] [Indexed: 01/12/2023]
Abstract
The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein-protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures. Database URL: https://github.com/liseda-lab/kgsim-benchmark.
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Affiliation(s)
- Carlota Cardoso
- Departamento de informática, LASIGE Faculdade de Ciências da Universidade de Lisboa, 1749 - 016 Lisboa, Portugal
| | - Rita T Sousa
- Departamento de informática, LASIGE Faculdade de Ciências da Universidade de Lisboa, 1749 - 016 Lisboa, Portugal
| | | | - Catia Pesquita
- Departamento de informática, LASIGE Faculdade de Ciências da Universidade de Lisboa, 1749 - 016 Lisboa, Portugal
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Ding Z, Kihara D. Computational identification of protein-protein interactions in model plant proteomes. Sci Rep 2019; 9:8740. [PMID: 31217453 PMCID: PMC6584649 DOI: 10.1038/s41598-019-45072-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 05/30/2019] [Indexed: 12/12/2022] Open
Abstract
Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of Arabidopsis thaliana and applied to three plants, Arabidopsis thaliana, Glycine max (soybean), and Zea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets of Arabidopsis. The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs in Arabidopsis thaliana, 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.
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Affiliation(s)
- Ziyun Ding
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, 45229, USA.
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Chen KH, Wang TF, Hu YJ. Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme. BMC Bioinformatics 2019; 20:308. [PMID: 31182027 PMCID: PMC6558856 DOI: 10.1186/s12859-019-2907-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Accepted: 05/17/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Although various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features. RESULTS We developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors. CONCLUSION We introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.
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Affiliation(s)
- Kuan-Hsi Chen
- College of Computer Science, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Tsai-Feng Wang
- Institute of Data Science and Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Yuh-Jyh Hu
- Institute of Biomedical Engineering, College of Computer Science, National Chiao Tung University, Hsinchu, 300, Taiwan.
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