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                    "pmcid": "PMC5793831",
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                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PMut: A web-based tool for the annotation of pathological variants on proteins, 2017 update",
                        "abstract": "We present here a full update of the PMut predictor, active since 2005 and with a large acceptance in the field of predicting Mendelian pathological mutations. PMut internal engine has been renewed, and converted into a fully featured standalone training and prediction engine that not only powers PMut web portal, but that can generate custom predictors with alternative training sets or validation schemas. PMut Web portal allows the user to perform pathology predictions, to access a complete repository of pre-calculated predictions, and to generate and validate new predictors. The default predictor performs with good quality scores (MCC values of 0.61 on 10-fold cross validation, and 0.42 on a blind test with SwissVar 2016 mutations).",
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                        "authors": [
                            {
                                "name": "Lopez-Ferrando V."
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                            {
                                "name": "Gazzo A."
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                            {
                                "name": "De La Cruz X."
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                            {
                                "name": "Orozco M."
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                            {
                                "name": "Gelpi J.L."
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        },
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                    "term": "Machine learning"
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                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
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                    "term": "Proteomics"
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                    "term": "Computational biology"
                },
                {
                    "uri": "http://edamontology.org/topic_0091",
                    "term": "Bioinformatics"
                },
                {
                    "uri": "http://edamontology.org/topic_0781",
                    "term": "Virology"
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            ],
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                "Windows"
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                {
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                    "doi": "10.1371/journal.pone.0289030",
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                    "pmcid": "PMC10365317",
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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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                        ],
                        "journal": "PLoS ONE"
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                }
            ],
            "credit": [
                {
                    "name": "Mark Edward M. Gonzales",
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                        "title": "Finding flexible patterns in unaligned protein sequences",
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                        "title": "A combined transmembrane topology and signal peptide prediction method",
                        "abstract": "An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at http://phobius.cgb.ki.se/ as well as at http://phobius.binf.ku.dk/ © 2004 Elsevier Ltd. All rights reserved.",
                        "date": "2004-05-14T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Kall L."
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                            {
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                            {
                                "name": "Sonnhammer E.L.L."
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                        "journal": "Journal of Molecular Biology"
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                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
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                        "authors": [
                            {
                                "name": "Madeira F."
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                            {
                                "name": "Pearce M."
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                            {
                                "name": "Tivey A.R.N."
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                            {
                                "name": "Basutkar P."
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                            {
                                "name": "Edbali O."
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                            {
                                "name": "Madhusoodanan N."
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                            {
                                "name": "Kolesnikov A."
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                            {
                                "name": "Lopez R."
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