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"operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1016/J.CSBJ.2022.11.009", "pmid": "36420168", "pmcid": "PMC9678803", "type": [], "version": null, "note": null, "metadata": { "title": "SoluProtMutDB: A manually curated database of protein solubility changes upon mutations", "abstract": "© 2022 The Author(s)Protein solubility is an attractive engineering target primarily due to its relation to yields in protein production and manufacturing. Moreover, better knowledge of the mutational effects on protein solubility could connect several serious human diseases with protein aggregation. However, we have limited understanding of the protein structural determinants of solubility, and the available data have mostly been scattered in the literature. Here, we present SoluProtMutDB – the first database containing data on protein solubility changes upon mutations. Our database accommodates 33 000 measurements of 17 000 protein variants in 103 different proteins. The database can serve as an essential source of information for the researchers designing improved protein variants or those developing machine learning tools to predict the effects of mutations on solubility. The database comprises all the previously published solubility datasets and thousands of new data points from recent publications, including deep mutational scanning experiments. Moreover, it features many available experimental conditions known to affect protein solubility. The datasets have been manually curated with substantial corrections, improving suitability for machine learning applications. The database is available at loschmidt.chemi.muni.cz/soluprotmutdb.", "date": "2022-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Velecky J." }, { "name": "Hamsikova M." }, { "name": "Stourac J." }, { "name": "Musil M." }, { "name": "Damborsky J." }, { "name": "Bednar D." }, { "name": "Mazurenko S." } ], "journal": "Computational and Structural Biotechnology Journal" } } ], "credit": [ { "name": "David Bednar", "email": "222755@mail.muni.cz", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Stanislav Mazurenko", "email": "mazurenko@mail.muni.cz", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Jan Velecký", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Marie Hamsikova", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "velda", "additionDate": "2023-01-25T22:49:31.072923Z", "lastUpdate": "2025-12-03T12:49:50.125351Z", "editPermission": { "type": "group", "authors": [ "velda", "m.musil" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "AggreProt", "description": "AggreProt is a web server to predict and engineer protein aggregation that provides protein aggregation propensity profile. Along with the protein solvent accessibility area and its transmembrane propensity, AggreProt predictions aid the users on designing soluble proteins.", "homepage": "https://loschmidt.chemi.muni.cz/aggreprot/", "biotoolsID": "aggreprotv1", "biotoolsCURIE": "biotools:aggreprotv1", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_0123", "term": "Protein properties" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "JavaScript", "Java" ], "license": "Proprietary", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Czech Republic" ], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/nar/gkae420", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "AggreProt: A web server for predicting and engineering aggregation prone regions in proteins", "abstract": "", "date": "2024-07-05T00:00:00Z", "citationCount": 17, "authors": [], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Joan Planas-Iglesias", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Simeon Borko", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Jiri Damborsky", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "David Bednar", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Jan Martinovic", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Jan Swiatkowski", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Matej Elias", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Martin Havlasek", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Ondrej Salamon", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Ekaterina Grakova", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Antonín Kunka", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Tomas Martinovic", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "m.musil", "additionDate": "2025-12-03T12:07:46.250923Z", "lastUpdate": "2025-12-03T12:07:46.253295Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "RAPTOR", "description": "RAPTOR intelligently recommends and benchmarks RNA-seq pipelines by analyzing 20+ data characteristics (biological variation, depth, zero-inflation, sample size). Multi-criteria scoring provides personalized pipeline recommendations from eight production-ready workflows (STAR-RSEM-DESeq2, Salmon-edgeR, Kallisto-Sleuth, etc.) in 30 seconds with clear reasoning. Comprehensive benchmarking quantifies accuracy, efficiency, and reproducibility metrics across all pipelines on user data with customizable priority modes.", "homepage": "https://github.com/AyehBlk/RAPTOR", "biotoolsID": "raptor-rnaseq", "biotoolsCURIE": "biotools:raptor-rnaseq", "version": [ "v2.1.0" ], "otherID": [ { "value": "doi:10.5281/zenodo.17607161", "type": "doi", "version": "v2.1.0" } ], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3218", "term": "Sequencing quality control" }, { "uri": "http://edamontology.org/operation_0314", "term": "Gene expression profiling" }, { "uri": "http://edamontology.org/operation_2238", "term": "Statistical calculation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" }, { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_3464", "term": "JSON" }, { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] }, { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" } ] } ], "note": "Comprehensive statistical characterization of RNA-seq datasets: library size statistics and variation, zero-inflation percentage, gene-level count distribution, biological coefficient of variation (BCV), mean-variance relationships, sequencing depth assessment, and experimental design balance.", "cmd": "raptor profile --counts your_counts.csv --metadata metadata.csv --output report.html" }, { "operation": [ { "uri": "http://edamontology.org/operation_2424", "term": "Comparison" }, { "uri": "http://edamontology.org/operation_2423", "term": "Prediction and recognition" }, { "uri": "http://edamontology.org/operation_2425", "term": "Optimisation and refinement" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0951", "term": "Statistical estimate score" }, "format": [ { "uri": "http://edamontology.org/format_3464", "term": "JSON" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" }, { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] }, { "data": { "uri": "http://edamontology.org/data_1772", "term": "Score" }, "format": [ { "uri": "http://edamontology.org/format_3464", "term": "JSON" }, { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2526", "term": "Text data" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" }, { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": "Intelligent recommendation system analyzing data characteristics (library size variation, zero-inflation, BCV, sample size, sequencing depth, design complexity) and matching them to pipeline strengths using multi-criteria scoring (0-200). Recommends from 8 pipelines: STAR-RSEM-DESeq2 (high accuracy), HISAT2-StringTie-Ballgown (novel transcripts), Salmon-edgeR (balanced), Kallisto-Sleuth (ultra-fast), STAR-HTSeq-limma (complex designs), STAR-featureCounts-NOISeq (non-parametric), Bowtie2-RSEM-EBSeq (Bayesian), HISAT2-Cufflinks-Cuffdiff (legacy). Priority modes: accuracy, speed, memory, or balanced.", "cmd": "raptor run --pipeline 3 --data your_fastq/ --output results/" }, { "operation": [ { "uri": "http://edamontology.org/operation_2424", "term": "Comparison" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_2428", "term": "Validation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_2182", "term": "FASTQ-like format (text)" } ] }, { "data": { "uri": "http://edamontology.org/data_0925", "term": "Sequence assembly" }, "format": [ { "uri": "http://edamontology.org/format_2305", "term": "GFF" } ] }, { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] }, { "data": { "uri": "http://edamontology.org/data_1772", "term": "Score" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" }, { "uri": "http://edamontology.org/format_3508", "term": "PDF" } ] }, { "data": { "uri": "http://edamontology.org/data_0928", "term": "Gene expression profile" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2082", "term": "Matrix" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" }, { "uri": "http://edamontology.org/format_3603", "term": "PNG" } ] } ], "note": "Comprehensive benchmarking framework comparing 8 RNA-seq pipelines. Evaluates: (1) Accuracy metrics: sensitivity, specificity, precision, F1, ROC AUC, concordance; (2) Efficiency metrics: runtime, memory, CPU utilization, disk space; (3) Reproducibility metrics: between-run concordance, fold-change correlation, p-value correlation. Benchmark datasets: simulated data (Polyester), SEQC/MAQC validation, and user data. Runtime: 2-4 hours (small datasets) to 8-24 hours (large datasets). Outputs: HTML dashboard, CSV/TSV tables, publication-ready figures.", "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" } ], "operatingSystem": [ "Linux" ], "language": [ "Python", "R", "Shell" ], "license": "MIT", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Belgium" ], "elixirCommunity": [], "link": [ { "url": "https://github.com/AyehBlk/RAPTOR", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/AyehBlk/RAPTOR", "type": "Source code", "note": null, "version": "v2.1.0" } ], "documentation": [ { "url": "https://github.com/AyehBlk/RAPTOR", "type": [ "Citation instructions", "Code of conduct", "Command-line options", "FAQ", "General", "Installation instructions", "Quick start guide", "Release notes", "Terms of use", "Training material", "User manual" ], "note": null } ], "publication": [], "credit": [], "owner": "AyehBlk", "additionDate": "2025-11-14T17:30:28.515260Z", "lastUpdate": "2025-12-03T09:32:53.698521Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "AutoProSeq - automatic classification of sequence types for prostate mpMRI", "description": "XGBoost-based automatic classification of sequence types for prostate mpMRI into T2W, DWI, ADC, DCE and others (any other sequence type) using metadata alone. Works as a CLI, allows direct application to DICOM datasets or CSV/tsv/Excel/parquet datasets, features integration for Orthanc and DICOM-web as RESTful APIs. This was applied to all data on ProCAncer-I to derive initial results for sequence types. Refines predictions using rule-based heuristics for T2W, DWI and ADC.", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/champ-autoproseq", "biotoolsID": "auto-prostate-sequence-classifier", "biotoolsCURIE": "biotools:auto-prostate-sequence-classifier", "version": [ "v0.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0968", "term": "Keyword" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "note": "The input can be provided as a DICOM dataset (which will require --dicom-recursion, details below or in the documentation) or as a single DICOM study. This command will print a CSV with 5 columns: i) patient_id, ii) study_uid, iii) series_uid, iv) classification from the XGBoost model, v) classification after heuristics-based corrections.\nThe sequence classification is performed between T2W, DWI, ADC, DCE or others. The provided <dicom-recursion> refers to the number of folders until a DICOM series is found. So if the provided input is a patient folder with nested studies with nested series, the level of DICOM recursion should be 2. If the provided input is a directory with patient folders, the level of DICOM recursion should be 3.", "cmd": "docker run -v <dir-with-dicom>:/data harbor.eucaim.cancerimage.eu/processing-tools/champ-autoproseq --dicom_recursion <dicom-recursion> --input_paths /data" } ], "toolType": [ "Web API", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "MIT", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/CCIG-Champalimaud/metadata-classification", "type": [ "Repository" ], "note": "Repository containing code, README and additional usage information for this tool." } ], "download": [], "documentation": [ { "url": "https://github.com/CCIG-Champalimaud/metadata-classification", "type": [ "Other" ], "note": "Repository containing the code, README and additional information on how to run the tool. If used as an API (when integrated with Orthanc or DICOM-web), the documentation is available upon launching the API at <api-url>/docs." } ], "publication": [ { "doi": "10.1186/s13244-025-01938-2", "pmid": "40146375", "pmcid": "PMC12187622", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning", "abstract": "", "date": "2025-12-01T00:00:00Z", "citationCount": 1, "authors": [], "journal": "Insights into Imaging" } } ], "credit": [ { "name": "José Guilherme de Almeida", "email": "jose.almeida@research.fchampalimaud.org", "url": "https://josegcpa.net", "orcidid": "https://orcid.org/0000-0002-1887-0157", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null }, { "name": "Nikolaos Papanikolaou", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-3298-2072", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": null } ], "owner": "josegcpa", "additionDate": "2025-12-02T17:37:57.215347Z", "lastUpdate": "2025-12-03T09:27:04.489071Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "IQA DCE tool", "description": "The purpose of the tool is to provide a complete framework for automatic detection of image quality for Breast DCE MR images. The assessment is performed on the first post-contrast dynamic phase in order to assess the most clinically relevant sequence among a number of identical acquisitions. 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Built in Python, it enables precise quantitative analysis of pediatric brain tumors in MRI scans to support clinical decision-making in both diagnosis and prognosis.", "homepage": "https://github.com/BIT-UPM/EUCAIM/pediatric_brain_tumor_segmenter", "biotoolsID": "pediatric_brain_tumor_segmenter", "biotoolsCURIE": "biotools:pediatric_brain_tumor_segmenter", "version": [ "v1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application", "Workflow", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3444", "term": "MRI" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Windows", "Mac", "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-SA-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://segmenter.hope4kids.io/", "type": [ "Other" ], "note": "Main app webpage" }, { "url": "https://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-Kids", "type": [ "Repository" ], "note": "GitHub repository" } ], "download": [], "documentation": [ { "url": "https://docs.hope4kids.io/HOPE-Segmenter-Kids/", "type": [ "API documentation", "Citation instructions", "Terms of use", "Code of conduct", "Release notes", "User manual", "General", "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1007/978-3-031-76163-8_20", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging", "abstract": "", "date": "2024-01-01T00:00:00Z", "citationCount": 2, "authors": [], "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)" } }, { "doi": "10.1109/ISBI56570.2024.10635469", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Enhancing Generalizability in Brain Tumor Segmentation: Model Ensemble with Adaptive Post-Processing", "abstract": "", "date": "2024-01-01T00:00:00Z", "citationCount": 1, "authors": [], "journal": "Proceedings - International Symposium on Biomedical Imaging" } }, { "doi": "10.48550/arXiv.2412.04094", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null }, { "doi": "10.48550/arXiv.2412.04111", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Daniel Capellán-Martín", "email": "daniel.capellan@upm.es", "url": null, "orcidid": "https://orcid.org/0000-0002-9743-0845", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Contributor", "Support" ], "note": null }, { "name": "Abhijeet Parida", "email": "pabhijeet@childrensnational.org", "url": null, "orcidid": "https://orcid.org/0000-0002-4978-0576", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Developer", "Contributor", "Support", "Maintainer" ], "note": null }, { "name": "Zhifan Jiang", "email": "zjiang@childrensnational.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Contributor", "Developer", "Support" ], "note": null }, { "name": "María Jesus Ledesma-Carbayo", "email": "mj.ledesma@upm.es", "url": null, "orcidid": "https://orcid.org/0000-0001-6846-3923", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact", "Contributor" ], "note": null }, { "name": "Marius George Linguraru", "email": "mlingura@childrensnational.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact", "Contributor" ], "note": null } ], "owner": "dani-capellan", "additionDate": "2025-10-28T18:31:05.926498Z", "lastUpdate": "2025-11-28T13:55:19.273705Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Prostate zone segmentation tool", "description": "This tool automatically segments the the prostate into two zones: the central and transition zones (TZ+CZ) and the peripheral zone (PZ). It takes as input a T2-weighted image and produces a segmentation in the same input format.\n\nThree datasets were used to train this model: ProstateX (n=152), Prostate158 (n=119) and ProstateNet (the ProCAncer-I dataset; n=532). We used the T2-weighted images available in each dataset and trained a standard three-class nnU-Net model with three classes: background, peripheral zone and the combination of the transition and central zones as annotated by radiologists. Using 149 cases from the three previously noted datasets, this model achieved DSC=0.81 (95% CI=[0.59, 0.92]) for PZ and 0.87 (95% CI=[0.60, 0.96]) for CZ+TZ. In a clinical validation using ProCAncer-I data, this model achieved, for PZ and TZ+CZ, DSC=0.65 and DSC=0.77, respectively, comparable to the observed between-radiologist DSC for a subset of cases (DSC=0.61 and DSC=0.73, respectively).", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/champ-prostate-zone-segmentation", "biotoolsID": "prostate_zone_segmentation_tool", "biotoolsCURIE": "biotools:prostate_zone_segmentation_tool", "version": [], "otherID": [], "relation": [ { "biotoolsID": "nnunet", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Image annotation for individual DICOM and Nifti images: Please note that it is easier to mount the input and output paths path and keep `--study_path` always as `/data/input` and `--output_dir` always as `/data/output`. This way only `--series_folders` requires any change. `--series_folders` can refer both to a Nifti file (if the input is Nifti) or the a DICOM series folder.\n\nIf the input is DICOM, `--is_dicom` should be used.", "cmd": "docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus=\"all\" test-eucaim:latest nnunet-predict --study_path /data/input --series_folders <series-folder> --output_dir /data/output" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Batch image annotation for DICOM and Nifti images using dataset JSON and data directory supports two methods: using a dataset JSON and using a data directory. \n\nThe dataset JSON-based method relies on having a dataset JSON which has a list of dictionaries that follow this structure: `{\"study_path\": str, \"series_folders\": [[str]], \"output_dir\": str}`. These are analogous to the previous modes. Be mindful that paths should be specified accordingly for the Docker file-system. The dataset JSON should be specified using `--data_json`.\n\nThe data directory-based method relies on having a directory structured as patient/study/series, where each series follows an nnU-Net-like convention (i.e. `<series_id>_0000`, `<series_id>_0001`, etc.). In this case, it should be `<series-id>_0000`. The data directory should be specified using `--data_dir` and requires the specification of an `--output_dir`.\n\nIf the input is DICOM, `--is_dicom` should be specified.", "cmd": "docker run --shm-size=1gb -v $(pwd)/example_dataset:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus=\"all\" test-eucaim:latest nnunet-predict-batch {{--data_json /data/input/<dataset_json>.json|--data_dir /data/input --output_dir /data/output}}" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/josegcpa/nnunet_serve", "type": [ "Repository" ], "note": "This is the nnunet_serve repository, containing the generic nnU-Net building and serving functionalities used by the Computational Clinical Imaging Group at Champalimaud Foundation. This should also be used as documentation." }, { "url": "https://docs.google.com/spreadsheets/d/1bcdhs9Xg2MxAH0WDGz08SjEVMFr3e-TP/edit?usp=sharing&ouid=111102743696297094705&rtpof=true&sd=true", "type": [ "Other" ], "note": "The EUCAIM model card for this model, containing details on model training, data and libraries." } ], "download": [], "documentation": [ { "url": "https://github.com/josegcpa/nnunet_serve", "type": [ "API documentation" ], "note": "This is the repository containing all of the code together with a comprehensive README for deployment and examplar files." } ], "publication": [ { "doi": "10.1016/j.compbiomed.2024.108216", "pmid": "38442555", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data", "abstract": "", "date": "2024-03-01T00:00:00Z", "citationCount": 9, "authors": [], "journal": "Computers in Biology and Medicine" } } ], "credit": [ { "name": "José Guilherme de Almeida", "email": "jose.almeida@research.fchampalimaud.org", "url": "https://josegcpa.net", "orcidid": "https://orcid.org/0000-0002-1887-0157", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Documentor", "Maintainer" ], "note": null }, { "name": "Nickolas Papanikolaou", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-3298-2072", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null }, { "name": "Nuno Rodrigues", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "josegcpa", "additionDate": "2025-11-07T18:15:49.962044Z", "lastUpdate": "2025-11-28T12:18:55.232382Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MITK", "description": "The Medical Imaging Interaction Toolkit (MITK) is a free open-source software for the development of interactive medical image processing software. Based on MITK, there is the MITK Workbench, a powerful and free application to view, process, and segment medical images.", "homepage": "https://www.mitk.org/", "biotoolsID": "mitk", "biotoolsCURIE": "biotools:mitk", "version": [ "v2023.04" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3551", "term": "nrrd" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" }, { "uri": "http://edamontology.org/format_3549", "term": "nii" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3551", "term": "nrrd" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" }, { "uri": "http://edamontology.org/format_3549", "term": "nii" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Workbench", "Desktop application", "Library" ], "topic": [], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "C++" ], "license": "BSD-3-Clause", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/MITK/MITK/", "type": [ "Repository" ], "note": null }, { "url": "https://lists.sourceforge.net/lists/listinfo/mitk-users", "type": [ "Mailing list" ], "note": "If you have any questions related to the use of or the development with MITK, please use the official mailing list.\n\nNote that you have to subscribe to the mailing list in order to send mails" } ], "download": [ { "url": "https://www.mitk.org/wiki/Downloads", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "https://docs.mitk.org/2023.04/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1016/j.media.2005.04.005", "pmid": "15896995", "pmcid": null, "type": [], "version": null, "note": "Nolden, Marco, et al. \"The Medical Imaging Interaction Toolkit: challenges and advances: 10 years of open-source development.\"\nInternational journal of computer assisted radiology and surgery 8 (2013): 607-620", "metadata": { "title": "The medical imaging interaction toolkit", "abstract": "Thoroughly designed, open-source toolkits emerge to boost progress in medical imaging. The Insight Toolkit (ITK) provides this for the algorithmic scope of medical imaging, especially for segmentation and registration. But medical imaging algorithms have to be clinically applied to be useful, which additionally requires visualization and interaction. The Visualization Toolkit (VTK) has powerful visualization capabilities, but only low-level support for interaction. In this paper, we present the Medical Imaging Interaction Toolkit (MITK). The goal of MITK is to significantly reduce the effort required to construct specifically tailored, interactive applications for medical image analysis. MITK allows an easy combination of algorithms developed by ITK with visualizations created by VTK and extends these two toolkits with those features, which are outside the scope of both. MITK adds support for complex interactions with multiple states as well as undo-capabilities, a very important prerequisite for convenient user interfaces. Furthermore, MITK facilitates the realization of multiple, different views of the same data (as a multiplanar reconstruction and a 3D rendering) and supports the visualization of 3D+t data, whereas VTK is only designed to create one kind of view of 2D or 3D data. MITK reuses virtually everything from ITK and VTK. Thus, it is not at all a competitor to ITK or VTK, but an extension, which eases the combination of both and adds the features required for interactive, convenient to use medical imaging software. MITK is an open-source project (www.mitk.org) © 2005 Elsevier B.V. All rights reserved.", "date": "2005-12-01T00:00:00Z", "citationCount": 378, "authors": [ { "name": "Wolf I." }, { "name": "Vetter M." }, { "name": "Wegner I." }, { "name": "Bottger T." }, { "name": "Nolden M." }, { "name": "Schobinger M." }, { "name": "Hastenteufel M." }, { "name": "Kunert T." }, { "name": "Meinzer H.-P." } ], "journal": "Medical Image Analysis" } }, { "doi": "10.1007/s11548-013-0840-8", "pmid": "23588509", "pmcid": null, "type": [], "version": null, "note": "Nolden, Marco, et al. \"The Medical Imaging Interaction Toolkit: challenges and advances: 10 years of open-source development.\"\nInternational journal of computer assisted radiology and surgery 8 (2013): 607-620", "metadata": { "title": "The medical imaging interaction toolkit: Challenges and advances: 10 years of open-source development", "abstract": "Purpose: The Medical Imaging Interaction Toolkit (MITK) has been available as open-source software for almost 10 years now. In this period the requirements of software systems in the medical image processing domain have become increasingly complex. The aim of this paper is to show how MITK evolved into a software system that is able to cover all steps of a clinical workflow including data retrieval, image analysis, diagnosis, treatment planning, intervention support, and treatment control. Methods: MITK provides modularization and extensibility on different levels. In addition to the original toolkit, a module system, micro services for small, system-wide features, a service-oriented architecture based on the Open Services Gateway initiative (OSGi) standard, and an extensible and configurable application framework allow MITK to be used, extended and deployed as needed. A refined software process was implemented to deliver high-quality software, ease the fulfillment of regulatory requirements, and enable teamwork in mixed-competence teams. Results: MITK has been applied by a worldwide community and integrated into a variety of solutions, either at the toolkit level or as an application framework with custom extensions. The MITK Workbench has been released as a highly extensible and customizable end-user application. Optional support for tool tracking, image-guided therapy, diffusion imaging as well as various external packages (e.g. CTK, DCMTK, OpenCV, SOFA, Python) is available. MITK has also been used in several FDA/CE-certified applications, which demonstrates the high-quality software and rigorous development process. Conclusions: MITK provides a versatile platform with a high degree of modularization and interoperability and is well suited to meet the challenging tasks of today's and tomorrow's clinically motivated research. © 2013 CARS.", "date": "2013-01-01T00:00:00Z", "citationCount": 354, "authors": [ { "name": "Nolden M." }, { "name": "Zelzer S." }, { "name": "Seitel A." }, { "name": "Wald D." }, { "name": "Muller M." }, { "name": "Franz A.M." }, { "name": "Maleike D." }, { "name": "Fangerau M." }, { "name": "Baumhauer M." }, { "name": "Maier-Hein L." }, { "name": "Maier-Hein K.H." }, { "name": "Meinzer H.-P." }, { "name": "Wolf I." } ], "journal": "International Journal of Computer Assisted Radiology and Surgery" } } ], "credit": [ { "name": "German Cancer Research Center (DKFZ)", "email": null, "url": "https://www.dkfz.de/de", "orcidid": null, "gridid": "grid.7497.d", "rorid": "04cdgtt98", "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "sschaum", "additionDate": "2023-06-26T12:38:15.108087Z", "lastUpdate": "2025-11-28T09:27:43.812008Z", "editPermission": { "type": "group", "authors": [ "ASHISR" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DICOM File Integrity Checker (EUCAIM-SW-002_T-01-01-002)", "description": "The tool performs a DICOM quality check in terms of correct number of files per sequence, corrupted files, precise directory hierarchy, separated dynamic series merging them, interest series filtering/selection by specific series description lists and diffusion sequence identification by b-values. It applies the desired changes to the dataset and generates a report containing information about the selected sequences, corrupted files, missing files and merged files.\n\nStatus: Deployed", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/artifacts-tab", "biotoolsID": "dicom_file_integrity_checker_by_gibi230", "biotoolsCURIE": "biotools:dicom_file_integrity_checker_by_gibi230", "version": [ "2.0.0", "2.1.0", "2.1.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0336", "term": "Format validation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] }, { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3464", "term": "JSON" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] }, { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" }, { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3464", "term": "JSON" } ] } ], "note": "For Data Holders (ingestion-tools)", "cmd": "docker run -it --rm --name my-container \\\n -v \"<input_path>:/input\" \\\n -v \"<output_path>:/output\" \\\n -v \"<config_path>:/config\" \\\n harbor.eucaim.cancerimage.eu/ingestion-tools/dicom_file_integrity_checker:latest" }, { "operation": [ { "uri": "http://edamontology.org/operation_0336", "term": "Format validation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] }, { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" }, { "uri": "http://edamontology.org/format_3620", "term": "xlsx" }, { "uri": "http://edamontology.org/format_3464", "term": "JSON" } ] } ], "note": "For Data Users (processing-tools)", "cmd": "docker run -it --rm --name my-container \\\n -v \"<input_path>:/input\" \\\n -v \"<output_path>:/output\" \\\n harbor.eucaim.cancerimage.eu/processing-tools/dicom_file_integrity_checker:latest \\\n /app/entrypoint.sh --config-string \"{'QA': {'sequence_selection': ['ALL'], 'modality_selection': ['ALL'], 'input_directory': 'dataset_id'}}\"" } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3316", "term": "Computer science" }, { "uri": "http://edamontology.org/topic_3077", "term": "Data acquisition" }, { "uri": "http://edamontology.org/topic_3071", "term": "Data management" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "Python" ], "license": "CC-BY-NC-ND-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/", "type": [ "Social media" ], "note": null }, { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/artifacts-tab", "type": [ "Software catalogue" ], "note": "Link to EUCAIM's Harbor for Data Holders" }, { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/dicom_file_integrity_checker/artifacts-tab", "type": [ "Software catalogue" ], "note": "Link to EUCAIM's Harbor for Data Users" } ], "download": [ { "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/artifacts-tab", "type": "Container file", "note": "EUCAIM user needed", "version": "2.1.0" } ], "documentation": [ { "url": "https://drive.google.com/file/d/1s8-nVAvSuAGTHWwun4AQcgHtRWclsFg2/view?usp=sharing", "type": [ "User manual" ], "note": null }, { "url": "https://drive.google.com/file/d/1pO_td7uvlsFWy6AZi8AOq3__hm4L2xSv/view?usp=drive_link", "type": [ "Terms of use" ], "note": null } ], "publication": [], "credit": [ { "name": "GIBI230 - HULAFE", "email": "gibi230@iislafe.es", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Pedro-Miguel Martinez-Girones", "email": "pedromiguel_martinez@iislafe.es", "url": null, "orcidid": "https://orcid.org/0000-0002-9506-9451", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Adrian Galiana-Bordera", "email": "adrian_galiana@iislafe.es", "url": null, "orcidid": "https://orcid.org/0000-0002-8324-8284", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Carina Soler-Pons", "email": "carina_soler@iislafe.es", "url": null, "orcidid": "https://orcid.org/0009-0000-2991-1391", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": null } ], "owner": "pedromiguel_martinez_HULAFE", "additionDate": "2025-04-15T17:42:40.968987Z", "lastUpdate": "2025-11-27T13:23:38.501118Z", "editPermission": { "type": "group", "authors": [ "casopon" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "FTAG-Finder", "description": "Families and TAG Finder: a Snakemake workflow to identify gene families and detect Tandemly Arrayed Genes.", "homepage": "https://gitlab.com/sortion/FTAG-Finder", "biotoolsID": "ftag-finder", "biotoolsCURIE": "biotools:ftag-finder", "version": [ "0.1.0" ], "otherID": [], "relation": [ { "biotoolsID": "blast", "type": "uses" }, { "biotoolsID": "mmseqs2", "type": "uses" }, { "biotoolsID": "diamond", "type": "uses" }, { "biotoolsID": "biopython", "type": "uses" }, { "biotoolsID": "mcl", "type": "uses" } ], "function": [], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_0797", "term": "Comparative genomics" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "AWK", "C", "Bash", "Python" ], "license": "MIT", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://gitlab.com/sortion/FTAG-Finder", "type": [ "Repository" ], "note": null }, { "url": "https://gitlab.com/sortion/FTAG-Finder/-/issues", "type": [ "Issue tracker" ], "note": null } ], "download": [ { "url": "https://gitlab.com/sortion/FTAG-Finder/-/releases", "type": "Downloads page", "note": null, "version": null } ], "documentation": [], "publication": [], "credit": [ { "name": "Carène Rizzon", "email": null, "url": "http://www.math-evry.cnrs.fr/members/crizzon/welcome", "orcidid": "https://orcid.org/0000-0003-1194-4130", "gridid": null, "rorid": "03jca6281", "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact" ], "note": null }, { "name": "Samuel Ortion", "email": null, "url": null, "orcidid": "https://orcid.org/0009-0001-0971-497X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Maintainer" ], "note": null } ], "owner": "sortion", "additionDate": "2025-11-27T10:42:10.165590Z", "lastUpdate": "2025-11-27T10:42:10.168319Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "BioContextAI Registry", "description": "The BioContextAI Registry is a community-driven catalog of Model Context Protocol (MCP) servers for agentic AI-supported biomedical research. 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All rights reserved.Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. 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