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                        "title": "DeepSig: Deep learning improves signal peptide detection in proteins",
                        "abstract": "Motivation The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website.",
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                        "title": "Nonpareil: A redundancy-based approach to assess the level of coverage in metagenomic datasets",
                        "abstract": "Motivation: Determining the fraction of the diversity within a microbial community sampled and the amount of sequencing required to cover the total diversity represent challenging issues for metagenomics studies. Owing to these limitations, central ecological questions with respect to the global distribution of microbes and the functional diversity of their communities cannot be robustly assessed.Results: We introduce Nonpareil, a method to estimate and project coverage in metagenomes. Nonpareil does not rely on high-quality assemblies, operational taxonomic unit calling or comprehensive reference databases; thus, it is broadly applicable to metagenomic studies. Application of Nonpareil on available metagenomic datasets provided estimates on the relative complexity of soil, freshwater and human microbiome communities, and suggested that ∼200 Gb of sequencing data are required for 95% abundance-weighted average coverage of the soil communities analyzed. © 2013 The Author 2013. Published by Oxford University Press. All rights reserved.",
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                        "title": "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields",
                        "abstract": "Motivation: Targeting peptides are the most important signal controlling the import of nuclear encoded proteins into mitochondria and plastids. In the lack of experimental information, their prediction is an essential step when proteomes are annotated for inferring both the localization and the sequence of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting peptides based on Grammatical-Restrained Hidden Conditional Random Fields. TPpred is trained on a non-redundant dataset of proteins where the presence of a target peptide was experimentally validated, comprising 297 sequences. When tested on the 297 positive and some other 8010 negative examples, TPpred outperformed available methods in both accuracy and Matthews correlation index (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%), TPpred is, therefore, well suited for large-scale analyses at the proteome level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis thaliana and yeast proteomes contain targeting peptides and are, therefore, likely to be localized in mitochondria and plastids. TPpred predictions correlate to a good extent with the experimental annotation of the subcellular localization, when available. TPpred was also trained and tested to predict the cleavage site of the organelle-targeting peptide: on this task, the average error of TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively. This value is lower than the error reported by other methods currently available. © 2013 The Author.",
                        "date": "2013-04-15T00:00:00Z",
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                    "metadata": {
                        "title": "ISPRED4: Interaction sites PREDiction in protein structures with a refining grammar model",
                        "abstract": "Motivation: The identification of protein-protein interaction (PPI) sites is an important step towards the characterization of protein functional integration in the cell complexity. Experimental methods are costly and time-consuming and computational tools for predicting PPI sites can fill the gaps of PPI present knowledge. Results: We present ISPRED4, an improved structure-based predictor of PPI sites on unbound monomer surfaces. ISPRED4 relies on machine-learning methods and it incorporates features extracted from protein sequence and structure. Cross-validation experiments are carried out on a new dataset that includes 151 high-resolution protein complexes and indicate that ISPRED4 achieves a per-residue Matthew Correlation Coefficient of 0.48 and an overall accuracy of 0.85. Benchmarking results show that ISPRED4 is one of the top-performing PPI site predictors developed so far.",
                        "date": "2017-06-01T00:00:00Z",
                        "citationCount": 25,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1007/978-3-642-35686-5_11",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Machine-learning methods to predict protein interaction sites in folded proteins",
                        "abstract": "A reliable predictor of protein-protein interaction sites is necessary to investigate and model protein functional interaction networks. Hidden Markov Support Vector Machines (HM-SVM) have been shown to be among the best performing methods on this task. Furthermore, it has been noted that the performance of a predictor improves when its input takes advantage of the difference between observed and predicted residue solvent accessibility. In this paper, for first time, we combine these elements and we present ISPRED2, a new HM-SVMbased method that overpasses the state of the art performance (Q2=0.71 and correlation=0.43). ISPRED2 consists of a sets of Python scripts aimed at integrating the different third-party software to obtain the final prediction. © Springer-Verlag 2012.",
                        "date": "2012-12-31T00:00:00Z",
                        "citationCount": 3,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Piovesan D."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"
                    }
                }
            ],
            "credit": [
                {
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                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
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                    "gridid": null,
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                    "typeRole": [
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                },
                {
                    "name": "Castrense Savojardo",
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        },
        {
            "name": "TPpred 3.0",
            "description": "Organelle-targeting peptide detection and cleavage-site prediction.",
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                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0422",
                            "term": "Protein cleavage site prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
                                "term": "Protein report"
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                                    "uri": "http://edamontology.org/format_2331",
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                    "note": "Predictio Protein sequence in FASTA format",
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                }
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                "Command-line tool"
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                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_0140",
                    "term": "Protein targeting and localisation"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
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            "download": [
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                    "version": null
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                    "url": "https://hub.docker.com/r/bolognabiocomp/tppred3",
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                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://tppred3.biocomp.unibo.it/tppred3/default/help",
                    "type": [
                        "General"
                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/BolognaBiocomp/tppred3",
                    "type": [
                        "Command-line options"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btv367",
                    "pmid": "26079349",
                    "pmcid": null,
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                        "Primary"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins",
                        "abstract": "Motivation: Molecular recognition of N-terminal targeting peptides is the most common mechanism controlling the import of nuclear-encoded proteins into mitochondria and chloroplasts. When experimental information is lacking, computational methods can annotate targeting peptides, and determine their cleavage sites for characterizing protein localization, function, and mature protein sequences. The problem of discriminating mitochondrial from chloroplastic propeptides is particularly relevant when annotating proteomes of photosynthetic Eukaryotes, endowed with both types of sequences. Results: Here, we introduce TPpred3, a computational method that given any Eukaryotic protein sequence performs three different tasks: (i) the detection of targeting peptides; (ii) their classification as mitochondrial or chloroplastic and (iii) the precise localization of the cleavage sites in an organelle-specific framework. Our implementation is based on our TPpred previously introduced. Here, we integrate a new N-to-1 Extreme Learning Machine specifically designed for the classification task (ii). For the last task, we introduce an organelle-specific Support Vector Machine that exploits sequence motifs retrieved with an extensive motif-discovery analysis of a large set of mitochondrial and chloroplastic proteins. We show that TPpred3 outperforms the state-of-the-art methods in all the three tasks.",
                        "date": "2015-03-25T00:00:00Z",
                        "citationCount": 38,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btu411",
                    "pmid": "24974200",
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs",
                        "abstract": "CONTACT: gigi@biocomp.unibo.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. SUMMARY: Targeting peptides are N-terminal sorting signals in proteins that promote their translocation to mitochondria through the interaction with different protein machineries. We recently developed TPpred, a machine learning-based method scoring among the best ones available to predict the presence of a targeting peptide into a protein sequence and its cleavage site. Here we introduce TPpred2 that improves TPpred performances in the task of identifying the cleavage site of the targeting peptides. TPpred2 is now available as a web interface and as a stand-alone version for users who can freely download and adopt it for processing large volumes of sequences. Availability and implementaion: TPpred2 is available both as web server and stand-alone version at http://tppred2.biocomp.unibo.it.",
                        "date": "2014-10-15T00:00:00Z",
                        "citationCount": 31,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics (Oxford, England)"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btt089",
                    "pmid": "23428638",
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields",
                        "abstract": "Motivation: Targeting peptides are the most important signal controlling the import of nuclear encoded proteins into mitochondria and plastids. In the lack of experimental information, their prediction is an essential step when proteomes are annotated for inferring both the localization and the sequence of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting peptides based on Grammatical-Restrained Hidden Conditional Random Fields. TPpred is trained on a non-redundant dataset of proteins where the presence of a target peptide was experimentally validated, comprising 297 sequences. When tested on the 297 positive and some other 8010 negative examples, TPpred outperformed available methods in both accuracy and Matthews correlation index (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%), TPpred is, therefore, well suited for large-scale analyses at the proteome level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis thaliana and yeast proteomes contain targeting peptides and are, therefore, likely to be localized in mitochondria and plastids. TPpred predictions correlate to a good extent with the experimental annotation of the subcellular localization, when available. TPpred was also trained and tested to predict the cleavage site of the organelle-targeting peptide: on this task, the average error of TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively. This value is lower than the error reported by other methods currently available. © 2013 The Author.",
                        "date": "2013-04-15T00:00:00Z",
                        "citationCount": 14,
                        "authors": [
                            {
                                "name": "Indio V."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1007/978-1-0716-1262-0_28",
                    "pmid": "34118055",
                    "pmcid": null,
                    "type": [
                        "Review",
                        "Benchmarking study"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Computer-Aided Prediction of Protein Mitochondrial Localization",
                        "abstract": "Protein sequences, directly translated from genomic data, need functional and structural annotation. Together with molecular function and biological process, subcellular localization is an important feature necessary for understanding the protein role and the compartment where the mature protein is active. In the case of mitochondrial proteins, their precursor sequences translated by the ribosome machinery include specific patterns from which it is possible not only to recognize their final destination within the organelle but also which of the mitochondrial subcompartments the protein is intended for. Four compartments are routinely discriminated, including the inner and the outer membranes, the intermembrane space, and the matrix. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequence and to discriminate their final destination in the organelle. We benchmark two of our methods on the general task of recognizing human mitochondrial proteins endowed with an experimentally characterized targeting peptide (TPpred3) and predicting which submitochondrial compartment is the final destination (DeepMito). We describe how to adopt our web servers in order to discriminate which human proteins are endowed with mitochondrial targeting peptides, the position of cleavage sites, and which submitochondrial compartment are intended for. By this, we add some other 1788 human proteins to the 450 ones already manually annotated in UniProt with a mitochondrial targeting peptide, providing for each of them also the characterization of the suborganellar localization.",
                        "date": "2021-01-01T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Tartari G."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Methods in Molecular Biology"
                    }
                }
            ],
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                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": null,
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                    ],
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                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": "http://biocomp.unibo.it/savojard/",
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        {
            "name": "BCov",
            "description": "Prediction of β-sheet topology.",
            "homepage": "http://biocomp.unibo.it/savojard/bcov/index.html",
            "biotoolsID": "bcov",
            "biotoolsCURIE": "biotools:bcov",
            "version": [
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                            "uri": "http://edamontology.org/operation_0476",
                            "term": "Ab initio structure prediction"
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                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1460",
                                "term": "Protein structure"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
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                            "data": {
                                "uri": "http://edamontology.org/data_1384",
                                "term": "Sequence alignment (protein)"
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                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1984",
                                    "term": "FASTA-aln"
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                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0906",
                                "term": "Protein interaction data"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": "Prediction Predicted or real secondary structure of the input protein Protein residue contact map",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_0082",
                    "term": "Structure prediction"
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            ],
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            "download": [
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            ],
            "documentation": [
                {
                    "url": "http://biocomp.unibo.it/savojard/bcov/index.html",
                    "type": [
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                    "note": null
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            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btt555",
                    "pmid": "24064422",
                    "pmcid": "PMC5994943",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BCov: A method for predicting β-sheet topology using sparse inverse covariance estimation and integer programming",
                        "abstract": "Motivation: Prediction of protein residue contacts, even at the coarsegrain level, can help in finding solutions to the protein structure prediction problem. Unlike α-helices that are locally stabilized, β-sheets result from pairwise hydrogen bonding of two or more disjoint regions of the protein backbone. The problem of predicting contacts among β-strands in proteins has been addressed by several supervised computational approaches. Recently, prediction of residue contacts based on correlated mutations has been greatly improved and finally allows the prediction of 3D structures of the proteins. Results: In this article, we describe BCov, which is the first unsuper vised method to predict the β-sheet topology starting from the protein sequence and its secondary structure. BCov takes advantage of the sparse inverse covariance estimation to define β-strand partner scores. Then an optimization based on integer programming is carried out to predict the β-sheet connectivity. When tested on the prediction of β-strand pairing, BCov scores with average values of Matthews Correlation Coefficient (MCC) and F1 equal to 0.56 and 0.61, respectively, on a non-redundant dataset of 916 protein chains known with atomic resolution. Our approach well compares with the state-of-the art methods trained so far for this specific task.",
                        "date": "2013-12-15T00:00:00Z",
                        "citationCount": 18,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
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                    "email": null,
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                    "typeEntity": "Institute",
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                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": "http://biocomp.unibo.it/savojard",
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
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        },
        {
            "name": "PhenPath",
            "description": "A tool for characterizing biological functions underlying different phenotypes.\n\na web server for associating phenotypes with molecular functional annotations.\n\nPhenPath includes a database and a tool:.\n'",
            "homepage": "http://phenpath.biocomp.unibo.it",
            "biotoolsID": "phenpath",
            "biotoolsCURIE": "biotools:phenpath",
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                            "term": "Data retrieval"
                        },
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                            "uri": "http://edamontology.org/operation_3501",
                            "term": "Enrichment analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3672",
                            "term": "Gene functional annotation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1150",
                                "term": "Disease ID"
                            },
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                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3668",
                                "term": "Disease name"
                            },
                            "format": []
                        },
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                            "data": {
                                "uri": "http://edamontology.org/data_3275",
                                "term": "Phenotype name"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1622",
                                "term": "Disease report"
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                            "format": []
                        }
                    ],
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                }
            ],
            "toolType": [
                "Database portal"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
                },
                {
                    "uri": "http://edamontology.org/topic_3305",
                    "term": "Public health and epidemiology"
                },
                {
                    "uri": "http://edamontology.org/topic_3325",
                    "term": "Rare diseases"
                }
            ],
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                "Linux",
                "Mac",
                "Windows"
            ],
            "language": [],
            "license": "CC-BY-NC-4.0",
            "collectionID": [
                "Rare Disease",
                "Bologna Biocomputing Group"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
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            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "http://phenpath.biocomp.unibo.it/phenpath/help_page.html",
                    "type": [
                        "User manual"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/S12864-019-5868-X",
                    "pmid": "31307376",
                    "pmcid": "PMC6631446",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PhenPath: A tool for characterizing biological functions underlying different phenotypes",
                        "abstract": "Background: Many diseases are associated with complex patterns of symptoms and phenotypic manifestations. Parsimonious explanations aim at reconciling the multiplicity of phenotypic traits with the perturbation of one or few biological functions. For this, it is necessary to characterize human phenotypes at the molecular and functional levels, by exploiting gene annotations and known relations among genes, diseases and phenotypes. This characterization makes it possible to implement tools for retrieving functions shared among phenotypes, co-occurring in the same patient and facilitating the formulation of hypotheses about the molecular causes of the disease. Results: We introduce PhenPath, a new resource consisting of two parts: PhenPathDB and PhenPathTOOL. The former is a database collecting the human genes associated with the phenotypes described in Human Phenotype Ontology (HPO) and OMIM Clinical Synopses. Phenotypes are then associated with biological functions and pathways by means of NET-GE, a network-based method for functional enrichment of sets of genes. The present version considers only phenotypes related to diseases. PhenPathDB collects information for 18 OMIM Clinical synopses and 7137 HPO phenotypes, related to 4292 diseases and 3446 genes. Enrichment of Gene Ontology annotations endows some 87.7, 86.9 and 73.6% of HPO phenotypes with Biological Process, Molecular Function and Cellular Component terms, respectively. Furthermore, 58.8 and 77.8% of HPO phenotypes are also enriched for KEGG and Reactome pathways, respectively. Based on PhenPathDB, PhenPathTOOL analyzes user-defined sets of phenotypes retrieving diseases, genes and functional terms which they share. This information can provide clues for interpreting the co-occurrence of phenotypes in a patient. Conclusions: The resource allows finding molecular features useful to investigate diseases characterized by multiple phenotypes, and by this, it can help researchers and physicians in identifying molecular mechanisms and biological functions underlying the concomitant manifestation of phenotypes. The resource is freely available at http://phenpath.biocomp.unibo.it.",
                        "date": "2019-07-16T00:00:00Z",
                        "citationCount": 8,
                        "authors": [
                            {
                                "name": "Babbi G."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "BMC Genomics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Pier Luigi Martelli",
                    "email": "pierluigi.martelli@unibo.it",
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                },
                {
                    "name": "Giulia Babbi",
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                    "rorid": null,
                    "fundrefid": null,
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            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2021-01-20T10:58:38Z",
            "lastUpdate": "2024-03-11T16:45:53.823541Z",
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            "confidence_flag": "tool"
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