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{
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    "list": [
        {
            "name": "Lion Localizer",
            "description": "Software tool for inferring the provenance of lions (Panthera leo) using mitochondrial DNA.",
            "homepage": "https://lionlocalizer.org",
            "biotoolsID": "lion_localizer",
            "biotoolsCURIE": "biotools:lion_localizer",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3192",
                            "term": "Sequence trimming"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0224",
                            "term": "Query and retrieval"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0487",
                            "term": "Haplotype mapping"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_0654",
                    "term": "DNA"
                },
                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
                }
            ],
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                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [],
            "license": null,
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
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            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/JHERED/ESAD072",
                    "pmid": "37952226",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Lion Localizer: A software tool for inferring the provenance of lions (Panthera leo) using mitochondrial DNA",
                        "abstract": "The illegal poaching of lions for their body parts poses a severe threat to lion populations across Africa. Poaching accounts for 35% of all human-caused lion deaths, with 51% attributed to retaliatory killings following livestock predation. In nearly half of the retaliatory killings, lion body parts are removed, suggesting that high demand for lion body parts may fuel killings attributed to human-lion conflict. Trafficked items are often confiscated in transit or destination countries far from their country of origin. DNA from lion parts may in some cases be the only available means for examining their geographic origins. In this paper, we present the Lion Localizer, a full-stack software tool that houses a comprehensive database of lion mitochondrial DNA (mtDNA) sequences sourced from previously published studies. The database covers 146 localities from across the African continent and India, providing information on the potential provenance of seized lion body parts. Lion mtDNA sequences of 350 or 1,140 bp corresponding to the cytochrome b region can be generated from lion products and queried against the Lion Localizer database. Using the query sequence, the Lion Localizer generates a listing of exact or partial matches, which are displayed on an interactive map of Africa. This allows for the rapid identification of potential regions and localities where lions have been or are presently being targeted by poachers. By examining the potential provenance of lion samples, the Lion Localizer serves as a valuable resource in the fight against lion poaching. The software is available at https://lionlocalizer.org.",
                        "date": "2024-03-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Au W.C."
                            },
                            {
                                "name": "Dures S.G."
                            },
                            {
                                "name": "Ishida Y."
                            },
                            {
                                "name": "Green C.E."
                            },
                            {
                                "name": "Zhao K."
                            },
                            {
                                "name": "Ogden R."
                            },
                            {
                                "name": "Roca A.L."
                            }
                        ],
                        "journal": "Journal of Heredity"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Alfred L Roca",
                    "email": "roca@illinois.edu",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
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                },
                {
                    "name": "Wesley C Au",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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            "owner": "Pub2Tools",
            "additionDate": "2024-04-19T11:31:13.116284Z",
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        },
        {
            "name": "MetGENE",
            "description": "Gene-centric metabolomics information retrieval tool.",
            "homepage": "https://bdcw.org/MetGENE/index.php",
            "biotoolsID": "metgene",
            "biotoolsCURIE": "biotools:metgene",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2422",
                            "term": "Data retrieval"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3436",
                            "term": "Aggregation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3695",
                            "term": "Filtering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3803",
                            "term": "Natural product identification"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Database portal"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3172",
                    "term": "Metabolomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
                },
                {
                    "uri": "http://edamontology.org/topic_3067",
                    "term": "Anatomy"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "PHP",
                "R"
            ],
            "license": null,
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            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/metabolomicsworkbench/MetGENE",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/GIGASCIENCE/GIAD089",
                    "pmid": "37983749",
                    "pmcid": "PMC10659118",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "MetGENE: gene-centric metabolomics information retrieval tool",
                        "abstract": "Background: Biomedical research often involves contextual integration of multimodal and multiomic data in search of mechanisms for improved diagnosis, treatment, and monitoring. Researchers need to access information from diverse sources, comprising data in various and sometimes incongruent formats. The downstream processing of the data to decipher mechanisms by reconstructing networks and developing quantitative models warrants considerable effort. Results: MetGENE is a knowledge-based, gene-centric data aggregator that hierarchically retrieves information about the gene(s), their related pathway(s), reaction(s), metabolite(s), and metabolomic studies from standard data repositories under one dashboard to enable ease of access through centralization of relevant information. We note that MetGENE focuses only on those genes that encode for proteins directly associated with metabolites. All other gene–metabolite associations are beyond the current scope of MetGENE. Further, the information can be contextualized by filtering by species, anatomy (tissue), and condition (disease or phenotype). Conclusions: MetGENE is an open-source tool that aggregates metabolite information for a given gene(s) and presents them in different computable formats (e.g., JSON) for further integration with other omics studies. MetGENE is available at https://bdcw.org/MetGENE/index.php.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Srinivasan S."
                            },
                            {
                                "name": "Maurya M.R."
                            },
                            {
                                "name": "Ramachandran S."
                            },
                            {
                                "name": "Fahy E."
                            },
                            {
                                "name": "Subramaniam S."
                            }
                        ],
                        "journal": "GigaScience"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Shankar Subramaniam",
                    "email": "shsubramaniam@ucsd.edu",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-8059-4659",
                    "gridid": null,
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                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Sumana Srinivasan",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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                    "typeEntity": "Person",
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                }
            ],
            "community": null,
            "owner": "Pub2Tools",
            "additionDate": "2024-04-19T11:28:09.722051Z",
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            "confidence_flag": "tool"
        },
        {
            "name": "Drava",
            "description": "Aligning human concepts with machine learning latent dimensions for the visual exploration of small multiples.",
            "homepage": "https://qianwen.info/DRAVA/",
            "biotoolsID": "drava",
            "biotoolsCURIE": "biotools:drava",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3935",
                            "term": "Dimensionality reduction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3891",
                            "term": "Essential dynamics"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_3318",
                    "term": "Physics"
                },
                {
                    "uri": "http://edamontology.org/topic_3382",
                    "term": "Imaging"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [],
            "license": null,
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": null,
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1145/3544548.3581127",
                    "pmid": "38074525",
                    "pmcid": "PMC10707479",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "DRAVA: Aligning Human Concepts with Machine Learning Latent Dimensions for the Visual Exploration of Small Multiples",
                        "abstract": "Latent vectors extracted by machine learning (ML) are widely used in data exploration (e.g., t-SNE) but suffer from a lack of interpretability. While previous studies employed disentangled representation learning (DRL) to enable more interpretable exploration, they often overlooked the potential mismatches between the concepts of humans and the semantic dimensions learned by DRL. To address this issue, we propose Drava, a visual analytics system that supports users in 1) relating the concepts of humans with the semantic dimensions of DRL and identifying mismatches, 2) providing feedback to minimize the mismatches, and 3) obtaining data insights from concept-driven exploration. Drava provides a set of visualizations and interactions based on visual piles to help users understand and refine concepts and conduct concept-driven exploration. Meanwhile, Drava employs a concept adaptor model to fine-tune the semantic dimensions of DRL based on user refinement. The usefulness of Drava is demonstrated through application scenarios and experimental validation.",
                        "date": "2023-04-19T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Wang Q."
                            },
                            {
                                "name": "L'Yi S."
                            },
                            {
                                "name": "Gehlenborg N."
                            }
                        ],
                        "journal": "Conference on Human Factors in Computing Systems - Proceedings"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Qianwen Wang",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-1728-4102",
                    "gridid": null,
                    "rorid": null,
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                    "typeRole": [],
                    "note": null
                }
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            "owner": "Pub2Tools",
            "additionDate": "2024-04-19T10:32:44.536054Z",
            "lastUpdate": "2024-04-19T10:32:44.538164Z",
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        },
        {
            "name": "Chemprop",
            "description": "Machine learning package for chemical property prediction.",
            "homepage": "http://github.com/chemprop/chemprop",
            "biotoolsID": "chemprop",
            "biotoolsCURIE": "biotools:chemprop",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3436",
                            "term": "Aggregation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3799",
                            "term": "Quantification"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3216",
                            "term": "Scaffolding"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3359",
                            "term": "Splitting"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_0593",
                    "term": "NMR"
                },
                {
                    "uri": "http://edamontology.org/topic_3314",
                    "term": "Chemistry"
                },
                {
                    "uri": "http://edamontology.org/topic_3407",
                    "term": "Endocrinology and metabolism"
                },
                {
                    "uri": "http://edamontology.org/topic_3047",
                    "term": "Molecular biology"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
            "language": [
                "Python",
                "Shell",
                "D"
            ],
            "license": "MIT",
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            "maturity": null,
            "cost": "Free of charge",
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            "elixirNode": [],
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            "link": [
                {
                    "url": "http://github.com/chemprop/chemprop_benchmark",
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                        "Repository"
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            ],
            "download": [],
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            "publication": [
                {
                    "doi": "10.1021/ACS.JCIM.3C01250",
                    "pmid": "38147829",
                    "pmcid": "PMC10777403",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Chemprop: A Machine Learning Package for Chemical Property Prediction",
                        "abstract": "Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.",
                        "date": "2024-01-08T00:00:00Z",
                        "citationCount": 7,
                        "authors": [
                            {
                                "name": "Heid E."
                            },
                            {
                                "name": "Greenman K.P."
                            },
                            {
                                "name": "Chung Y."
                            },
                            {
                                "name": "Li S.-C."
                            },
                            {
                                "name": "Graff D.E."
                            },
                            {
                                "name": "Vermeire F.H."
                            },
                            {
                                "name": "Wu H."
                            },
                            {
                                "name": "Green W.H."
                            },
                            {
                                "name": "McGill C.J."
                            }
                        ],
                        "journal": "Journal of Chemical Information and Modeling"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Charles J. McGill",
                    "email": "mcgillc2@vcu.edu",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-2704-7717",
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            "owner": "Pub2Tools",
            "additionDate": "2024-04-19T10:29:44.116723Z",
            "lastUpdate": "2024-04-19T10:29:44.118952Z",
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        },
        {
            "name": "MCPtaggR",
            "description": "R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison.",
            "homepage": "https://github.com/tomoyukif/MCPtaggR",
            "biotoolsID": "mcptaggr",
            "biotoolsCURIE": "biotools:mcptaggr",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3196",
                            "term": "Genotyping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3933",
                            "term": "Demultiplexing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0484",
                            "term": "SNP detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0524",
                            "term": "De-novo assembly"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Library"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
                },
                {
                    "uri": "http://edamontology.org/topic_0780",
                    "term": "Plant biology"
                },
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_3175",
                    "term": "Structural variation"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "R"
            ],
            "license": "GPL-3.0",
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
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            "elixirPlatform": [],
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            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "https://tomoyukif.github.io/MCPtaggR/",
                    "type": [
                        "User manual"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/DNARES/DSAD027",
                    "pmid": "38134958",
                    "pmcid": "PMC10799318",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison",
                        "abstract": "Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.",
                        "date": "2024-02-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Furuta T."
                            },
                            {
                                "name": "Yamamoto T."
                            }
                        ],
                        "journal": "DNA Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Tomoyuki Furuta",
                    "email": "f.tomoyuki@okayama-u.ac.jp",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0869-6626",
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            "owner": "Pub2Tools",
            "additionDate": "2024-04-19T10:26:21.921850Z",
            "lastUpdate": "2024-04-19T10:26:21.924007Z",
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        },
        {
            "name": "MetalDock",
            "description": "An open-access docking tool for easy and reproducible docking of metal complexes.",
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                        {
                            "uri": "http://edamontology.org/operation_1831",
                            "term": "Metal-bound cysteine detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_1830",
                            "term": "Free cysteine detection"
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                    "metadata": {
                        "title": "MetalDock: An Open Access Docking Tool for Easy and Reproducible Docking of Metal Complexes",
                        "abstract": "Despite the proven potential of metal complexes as therapeutics, the lack of computational tools available for the high-throughput screening of their interactions with proteins is a limiting factor toward clinical developments. To address this challenge, we introduce MetalDock, an easy-to-use, open access docking software for docking metal complexes to proteins. Our tool integrates the AutoDock docking engine with three well-known quantum software packages to automate the docking of metal-organic complexes to proteins. We used a Monte Carlo sampling scheme to obtain the missing Lennard-Jones parameters for 12 metal atom types and demonstrated that these parameters generalize exceptionally well. Our results show that the poses obtained by MetalDock are highly accurate, as they predict the binding geometries experimentally determined by crystal structures with high spatial reproducibility. Three different case studies are presented that demonstrate the versatility of MetalDock for the docking of diverse metal-organic compounds to different biomacromolecules, including nucleic acids.",
                        "date": "2023-12-25T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Hakkennes M.L.A."
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                            {
                                "name": "Buda F."
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                            {
                                "name": "Bonnet S."
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                        "journal": "Journal of Chemical Information and Modeling"
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                {
                    "name": "Francesco Buda",
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                        "title": "Clumppling: cluster matching and permutation program with integer linear programming",
                        "abstract": "Motivation: In the mixed-membership unsupervised clustering analyses commonly used in population genetics, multiple replicate data analyses can differ in their clustering solutions. Combinatorial algorithms assist in aligning clustering outputs from multiple replicates so that clustering solutions can be interpreted and combined across replicates. Although several algorithms have been introduced, challenges exist in achieving optimal alignments and performing alignments in reasonable computation time. Results: We present Clumppling, a method for aligning replicate solutions in mixed-membership unsupervised clustering. The method uses integer linear programming for finding optimal alignments, embedding the cluster alignment problem in standard combinatorial optimization frameworks. In example analyses, we find that it achieves solutions with preferred values of a desired objective function relative to those achieved by Pong and that it proceeds with less computation time than Clumpak. It is also the first method to permit alignments across replicates with multiple arbitrary values of the number of clusters K.",
                        "date": "2024-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Liu X."
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                            {
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                            {
                                "name": "Rosenberg N.A."
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            "name": "ImageNomer",
            "description": "Description of a functional connectivity and omics analysis tool and case study identifying a race confound.",
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                    "term": "Genotype and phenotype"
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                    "term": "Medical imaging"
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                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
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                    "doi": "10.1016/J.YNIRP.2023.100191",
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                    "metadata": {
                        "title": "ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound",
                        "abstract": "Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10–15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Orlichenko A."
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                            {
                                "name": "Daly G."
                            },
                            {
                                "name": "Zhou Z."
                            },
                            {
                                "name": "Liu A."
                            },
                            {
                                "name": "Shen H."
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                            {
                                "name": "Deng H.-W."
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                            {
                                "name": "Wang Y.-P."
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                        ],
                        "journal": "Neuroimage: Reports"
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            "credit": [
                {
                    "name": "Anton Orlichenko",
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                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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                {
                    "uri": "http://edamontology.org/topic_2229",
                    "term": "Cell biology"
                },
                {
                    "uri": "http://edamontology.org/topic_3382",
                    "term": "Imaging"
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                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
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                    "note": null,
                    "metadata": {
                        "title": "Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images",
                        "abstract": "In spatially resolved transcriptomics, Stereo-seq facilitates the analysis of large tissues at the single-cell level, offering subcellular resolution and centimeter-level field-of-view. Our previous work on StereoCell introduced a one-stop software using cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With advancements allowing the acquisition of cell boundary information, such as cell membrane/wall staining images, we updated our software to a new version, STCellbin. Using cell nuclei staining images, STCellbin aligns cell membrane/wall staining images with spatial gene expression maps. Advanced cell segmentation ensures the detection of accurate cell boundaries, leading to more reliable single-cell spatial gene expression profiles. We verified that STCellbin can be applied to mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets, outperforming other methods. The improved capability of capturing single-cell gene expression profiles results in a deeper understanding of the contribution of single-cell phenotypes to tissue biology.",
                        "date": "2024-02-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Zhang B."
                            },
                            {
                                "name": "Li M."
                            },
                            {
                                "name": "Kang Q."
                            },
                            {
                                "name": "Deng Z."
                            },
                            {
                                "name": "Qin H."
                            },
                            {
                                "name": "Su K."
                            },
                            {
                                "name": "Feng X."
                            },
                            {
                                "name": "Chen L."
                            },
                            {
                                "name": "Liu H."
                            },
                            {
                                "name": "Fang S."
                            },
                            {
                                "name": "Zhang Y."
                            },
                            {
                                "name": "Li Y."
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                            {
                                "name": "Brix S."
                            },
                            {
                                "name": "Xu X."
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                        ],
                        "journal": "GigaByte"
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        {
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                            "term": "Deposition"
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                    "term": "Cell biology"
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                    "term": "Drug discovery"
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                    "term": "Model organisms"
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                    "metadata": {
                        "title": "NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation",
                        "abstract": "The assembly of biomolecular condensate in eukaryotic cells and the accumulation of amyloid deposits in neurons are processes involving the nucleation and growth (NAG) of new protein phases. To therapeutically target protein phase separation, drug candidates are tested in in vitro assays that monitor the increase in the mass or size of the new phase. Limited mechanistic insight is, however, provided if empirical or untestable kinetic models are fitted to these progress curves. Here we present the web server NAGPKin that quantifies NAG rates using mass-based or size-based progress curves as the input data. A report is generated containing the fitted NAG parameters and elucidating the phase separation mechanisms at play. The NAG parameters can be used to predict particle size distributions of, for example, protein droplets formed by liquid-liquid phase separation (LLPS) or amyloid fibrils formed by protein aggregation. Because minimal intervention is required from the user, NAGPKin is a good platform for standardized reporting of LLPS and protein self-assembly data. NAGPKin is useful for drug discovery as well as for fundamental studies on protein phase separation. NAGPKin is freely available (no login required) at https://nagpkin.i3s.up.pt.",
                        "date": "2024-03-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Sarkany Z."
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                            {
                                "name": "Figueiredo F."
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                            {
                                "name": "Macedo-Ribeiro S."
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                            {
                                "name": "Martins P.M."
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                        "journal": "Molecular biology of the cell"
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