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            "name": "NetMHCIIpan",
            "description": "Predict binding of peptides to MHC class II molecules. The predictions are available for all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ, as well as mouse molecules.",
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                            "uri": "http://edamontology.org/operation_0416",
                            "term": "Epitope mapping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2575",
                            "term": "Protein binding site prediction"
                        }
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                        {
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                                "uri": "http://edamontology.org/data_2044",
                                "term": "Sequence"
                            },
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                                    "term": "FASTA"
                                }
                            ]
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                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
                            },
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                                    "term": "HTML"
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                    "note": "Predicts binding of peptides to MHC class II molecules.",
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                    "term": "Protein binding sites"
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                {
                    "url": "http://www.cbs.dtu.dk/services",
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                {
                    "url": "https://www-nature-com.proxy1-bib.sdu.dk/articles/s42003-023-04749-7",
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                        "Other"
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                    "url": "http://www.cbs.dtu.dk/services/NetMHCIIpan/",
                    "type": [
                        "General"
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            "publication": [
                {
                    "doi": "10.1007/s00251-013-0720-y",
                    "pmid": "23900783",
                    "pmcid": "PMC3809066",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ",
                        "abstract": "Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide-MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0. © 2013 Springer-Verlag Berlin Heidelberg.",
                        "date": "2013-10-01T00:00:00Z",
                        "citationCount": 216,
                        "authors": [
                            {
                                "name": "Karosiene E."
                            },
                            {
                                "name": "Rasmussen M."
                            },
                            {
                                "name": "Blicher T."
                            },
                            {
                                "name": "Lund O."
                            },
                            {
                                "name": "Buus S."
                            },
                            {
                                "name": "Nielsen M."
                            }
                        ],
                        "journal": "Immunogenetics"
                    }
                }
            ],
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                {
                    "name": "Edita Karosiene",
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                    "name": "CBS",
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                },
                {
                    "name": "Morten Nielsen",
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            "name": "MolStar",
            "description": "Mol* is a web application for visualization of biomacromolecular structures including experimental data (for example electron densities) and annotations. Can visualize also molecular dynamics.",
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                            "data": {
                                "uri": "http://edamontology.org/data_1460",
                                "term": "Protein structure"
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                                    "term": "mmCIF"
                                },
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                                    "term": "PDB"
                                }
                            ]
                        }
                    ],
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                            "data": {
                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_3603",
                                    "term": "PNG"
                                },
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                                    "uri": "http://edamontology.org/format_3579",
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            ],
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                "Web application"
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                    "term": "Computational biology"
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                "Linux",
                "Windows"
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                "Other"
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                "PDBe Tools",
                "EMBL-EBI"
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                "Czech Republic"
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                "3D-BioInfo"
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                    "url": "https://github.com/molstar",
                    "type": "Source code",
                    "note": null,
                    "version": null
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            ],
            "documentation": [
                {
                    "url": "https://molstar.org/viewer-docs/",
                    "type": [
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                {
                    "doi": "10.1093/nar/gkab314",
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Mol∗Viewer: Modern web app for 3D visualization and analysis of large biomolecular structures",
                        "abstract": "Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomplished with the introduction of the LiteMol suite and NGL Viewers. Thereafter, Mol∗ development was jointly initiated by PDBe and RCSB PDB to combine and build on the strengths of LiteMol (developed by PDBe) and NGL (developed by RCSB PDB). The web-native Mol∗ Viewer enables 3D visualization and streaming of macromolecular coordinate and experimental data, together with capabilities for displaying structure quality, functional, or biological context annotations. High-performance graphics and data management allows users to simultaneously visualise up to hundreds of (superimposed) protein structures, stream molecular dynamics simulation trajectories, render cell-level models, or display huge I/HM structures. It is the primary 3D structure viewer used by PDBe and RCSB PDB. It can be easily integrated into third-party services. Mol∗ Viewer is open source and freely available at https://molstar.org/.",
                        "date": "2021-07-02T00:00:00Z",
                        "citationCount": 468,
                        "authors": [
                            {
                                "name": "Sehnal D."
                            },
                            {
                                "name": "Bittrich S."
                            },
                            {
                                "name": "Deshpande M."
                            },
                            {
                                "name": "Svobodova R."
                            },
                            {
                                "name": "Berka K."
                            },
                            {
                                "name": "Bazgier V."
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                            {
                                "name": "Velankar S."
                            },
                            {
                                "name": "Burley S.K."
                            },
                            {
                                "name": "Koca J."
                            },
                            {
                                "name": "Rose A.S."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
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                    "name": "David Sehnal",
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            "additionDate": "2020-10-14T12:12:55Z",
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            "homepage_status": 0,
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        },
        {
            "name": "Omada",
            "description": "We have developed a suite of tools to automate RNAseq clustering processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found.",
            "homepage": "https://bioconductor.org/packages/release/bioc/html/omada.html",
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            "biotoolsCURIE": "biotools:omada",
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_3432",
                            "term": "Clustering"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2603",
                                "term": "Expression data"
                            },
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                    "note": null,
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            ],
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                "R"
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                    "doi": "10.1101/2022.12.19.519427",
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                {
                    "name": "Sokratis Kariotis",
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                    "note": "Scientist at A*STAR, bioinformatics"
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        {
            "name": "SKM",
            "description": "Stress Knowledge Map:  A compilation of knowledge on mechanisms underlying responses of plants to stress, the so called stress signalling network.",
            "homepage": "https://skm.nib.si/",
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                    "biotoolsID": "gomapman",
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                            "data": {
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            ],
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            ],
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            "documentation": [
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                    "url": "https://skm.nib.si/documentation",
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            ],
            "publication": [
                {
                    "doi": "10.1016/j.xplc.2024.100920",
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                    "pmcid": null,
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                    "version": null,
                    "note": "Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses",
                    "metadata": {
                        "title": "Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses",
                        "abstract": "Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.",
                        "date": "2024-06-10T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Bleker C."
                            },
                            {
                                "name": "Ramsak"
                            },
                            {
                                "name": "Bittner A."
                            },
                            {
                                "name": "Podpecan V."
                            },
                            {
                                "name": "Zagorscak M."
                            },
                            {
                                "name": "Wurzinger B."
                            },
                            {
                                "name": "Baebler"
                            },
                            {
                                "name": "Petek M."
                            },
                            {
                                "name": "Kriznik M."
                            },
                            {
                                "name": "van Dieren A."
                            },
                            {
                                "name": "Gruber J."
                            },
                            {
                                "name": "Afjehi-Sadat L."
                            },
                            {
                                "name": "Weckwerth W."
                            },
                            {
                                "name": "Zupanic A."
                            },
                            {
                                "name": "Teige M."
                            },
                            {
                                "name": "Vothknecht U.C."
                            },
                            {
                                "name": "Gruden K."
                            }
                        ],
                        "journal": "Plant Communications"
                    }
                }
            ],
            "credit": [
                {
                    "name": "National Institute of Biology, Department of Biotechnology and Systems Biology",
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                    "url": "http://www.nib.si/",
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                    "name": "Carissa Bleker",
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        },
        {
            "name": "Integron Finder",
            "description": "A tool to detect Integron in DNA sequences.",
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                            "term": "Sequence motif recognition"
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                    "output": [
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                            "data": {
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                                "term": "Feature table"
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                    "note": "Single fasta file",
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                "Command-line tool"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0085",
                    "term": "Functional genomics"
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                {
                    "uri": "http://edamontology.org/topic_0798",
                    "term": "Mobile genetic elements"
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                    "uri": "http://edamontology.org/topic_3047",
                    "term": "Molecular biology"
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            "link": [
                {
                    "url": "https://github.com/gem-pasteur/Integron_Finder/",
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            "documentation": [
                {
                    "url": "http://integronfinder.readthedocs.org/",
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                        "General"
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                    "note": null
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            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkw319",
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                        "Primary"
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                    "metadata": {
                        "title": "Identification and analysis of integrons and cassette arrays in bacterial genomes",
                        "abstract": "Integrons recombine gene arrays and favor the spread of antibiotic resistance. Their broader roles in bacterial adaptation remain mysterious, partly due to lack of computational tools. We made a program-IntegronFinder-to identify integrons with high accuracy and sensitivity. IntegronFinder is available as a standalone program and as a web application. It searches for attC sites using covariance models, for integron-integrases using HMM profiles, and for other features (promoters, attI site) using pattern matching. We searched for integrons, integron-integrases lacking attC sites, and clusters of attC sites lacking a neighboring integron-integrase in bacterial genomes. All these elements are especially frequent in genomes of intermediate size. They are missing in some key phyla, such as α-Proteobacteria, which might reflect selection against cell lineages that acquire integrons. The similarity between attC sites is proportional to the number of cassettes in the integron, and is particularly low in clusters of attC sites lacking integron-integrases. The latter are unexpectedly abundant in genomes lacking integron-integrases or their remains, and have a large novel pool of cassettes lacking homologs in the databases. They might represent an evolutionary step between the acquisition of genes within integrons and their stabilization in the new genome.",
                        "date": "2016-06-02T00:00:00Z",
                        "citationCount": 193,
                        "authors": [
                            {
                                "name": "Cury J."
                            },
                            {
                                "name": "Jove T."
                            },
                            {
                                "name": "Touchon M."
                            },
                            {
                                "name": "Neron B."
                            },
                            {
                                "name": "Rocha E.P."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.3390/microorganisms10020224",
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                    "pmcid": null,
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                        "Primary"
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                    "metadata": {
                        "title": "Integron Functionality and Genome Innovation: An Update on the Subtle and Smart Strategy of Integrase and Gene Cassette Expression Regulation",
                        "abstract": "Integrons are considered hot spots for bacterial evolution, since these platforms allow one-step genomic innovation by capturing and expressing genes that provide advantageous novelties, such as antibiotic resistance. The acquisition and shuffling of gene cassettes featured by integrons enable the population to rapidly respond to changing selective pressures. However, in order to avoid deleterious effects and fitness burden, the integron activity must be tightly controlled, which happens in an elegant and elaborate fashion, as discussed in detail in the present review. Here, we aimed to provide an up‐to‐date overview of the complex regulatory networks that permeate the expression and functionality of integrons at both transcriptional and translational levels. It was possible to compile strong shreds of evidence clearly proving that these versatile platforms include functions other than acquiring and expressing gene cassettes. The well‐balanced mechanism of integron expression is intricately related with environmental signals, host cell physiology, fitness, and survival, ultimately leading to adaptation on the demand.",
                        "date": "2022-02-01T00:00:00Z",
                        "citationCount": 9,
                        "authors": [
                            {
                                "name": "Fonseca E.L."
                            },
                            {
                                "name": "Vicente A.C."
                            }
                        ],
                        "journal": "Microorganisms"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Institut Pasteur",
                    "email": null,
                    "url": "http://www.pasteur.fr/en",
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                    "name": "Jean Cury",
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                    "note": "CNRS - UMR 352"
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                    "name": "Jean Cury",
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                    "note": "Predicts the host genus of a phage given its receptor-binding protein sequences",
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                    "term": "Proteomics"
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                    "doi": "10.1371/journal.pone.0289030",
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                    "pmcid": "PMC10365317",
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                    "version": null,
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                    "metadata": {
                        "title": "Protein embeddings improve phage-host interaction prediction",
                        "abstract": "With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage’s receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.",
                        "date": "2023-07-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Gonzales M.E.M."
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                            {
                                "name": "Ureta J.C."
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                            {
                                "name": "Shrestha A.M.S."
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                        ],
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                }
            ],
            "credit": [
                {
                    "name": "Mark Edward M. Gonzales",
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                    "url": "https://github.com/memgonzales",
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                    "note": "Research Assistant, Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines"
                },
                {
                    "name": "Jennifer C. Ureta",
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                    "url": "https://scholar.google.com/citations?user=v0Tf_u4AAAAJ&hl=en",
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                {
                    "name": "Anish M.S. Shrestha",
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                    "url": "https://a-transposable-element.com/",
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            "download": [
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                    "type": "Downloads page",
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                    "metadata": {
                        "title": "MView: A web-compatible database search or multiple alignment viewer",
                        "abstract": "Summary: MView is a tool for converting the results of a sequence database search into the form of a coloured multiple alignment of hits stacked against the query. Alternatively, an existing multiple alignment can be processed. In either case, the output is simply HTML, so the result is platform independent and does not require a separate application or applet to be loaded. Availability: Free from http://www.sander.ebi.ac.uk/mview/ subject to copyright restrictions. Contact: brown@@@ebi.ac.uk.",
                        "date": "1998-01-01T00:00:00Z",
                        "citationCount": 289,
                        "authors": [
                            {
                                "name": "Brown N.P."
                            },
                            {
                                "name": "Leroy C."
                            },
                            {
                                "name": "Sander C."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/nar/gkae241",
                    "pmid": "38597606",
                    "pmcid": null,
                    "type": [
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                    ],
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                },
                {
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