{"id":61,"date":"2023-11-20T15:59:35","date_gmt":"2023-11-20T12:59:35","guid":{"rendered":"https:\/\/gomedprecision.gr\/?page_id=61"},"modified":"2026-02-19T12:52:52","modified_gmt":"2026-02-19T09:52:52","slug":"%cf%80%ce%b1%cf%81%ce%b1%ce%b4%ce%bf%cf%84%ce%ad%ce%b1","status":"publish","type":"page","link":"https:\/\/gomedprecision.gr\/en\/%cf%80%ce%b1%cf%81%ce%b1%ce%b4%ce%bf%cf%84%ce%ad%ce%b1\/","title":{"rendered":"Deliverables"},"content":{"rendered":"

Project deliverables<\/p>\n\n\n\n

Work Packages<\/strong><\/td>Deliverable<\/strong><\/td>Type<\/strong><\/td>Due Date\n(in months)<\/strong><\/td><\/tr>
1<\/td>D1.1: Situational Analysis and Documentation of the Current Landscape<\/td>Report<\/a><\/td>20<\/td><\/tr>
1<\/td>D1.2: Prototype Version of the Rare Disease Registry Ecosystem<\/td>Prototype<\/a><\/td>20<\/td><\/tr>
1<\/td>D1.3: Exploratory Data Analysis of Rare Disease Registries<\/td>Report<\/a><\/td>28<\/td><\/tr>
1<\/td>D1.4: Final Integrated Version of the Rare Disease Registry Ecosystem<\/td>\u03a0\u03b5\u03c1\u03b9\u03b3\u03c1\u03b1\u03c6\u03ae<\/a>
Software<\/a><\/td>		28<\/td><\/tr>
2<\/td>D2.1: Implementation of VUS Analysis Methods in Rare Diseases \u2013 Conference Presentation<\/td>Other<\/a><\/td>14<\/td><\/tr>
2<\/td>D2.2: Metabolomic Profiling of Rare Disease Patients \u2013 Conference Presentation<\/td>Other<\/a><\/td>24<\/td><\/tr>
3<\/td>D2.3: Identification of Candidate Causative Genes in Rare Disease Patients \u2013 Conference Presentation<\/td>Report<\/a><\/td>14<\/td><\/tr>
3<\/td>D3.1: Segregation Analysis of Variants of Uncertain Significance (VUS) in Cancer-Predisposed Families<\/td>Other<\/a><\/td>28<\/td><\/tr>
3<\/td>D3.2: Re-Evaluation of VUS and Polygenic Risk Scores (PRS) in Hereditary Cancer \u2013 Conference Presentation<\/td>Other<\/a><\/td>28<\/td><\/tr>
4<\/td>D4.1: Functional Characterization of VUS in Familial Breast Cancer \u2013 Conference Presentation<\/td>Other<\/a><\/td>14<\/td><\/tr>
4<\/td>D4.2: Functional Characterization of VUS in Rare Diseases \u2013 Conference Presentation<\/td>Other<\/a><\/td>28<\/td><\/tr>
5<\/td>D5.1: Risk Stratification Using Polygenic Risk Scores<\/td>Report<\/a><\/td>20<\/td><\/tr>
5<\/td>D5.2: Risk Stratification Using Multi-Omic Scores \u2013 Scientific Publication<\/td>Publication<\/a><\/td>28<\/td><\/tr>
6<\/td>D6.1: Development of Machine Learning Models for Patient Stratification and Risk Prediction<\/td>Report<\/a><\/td>18<\/td><\/tr>
6<\/td>D6.2: Optimization and Adaptation of Trained Machine Learning Models<\/td>Report<\/a><\/td>28<\/td><\/tr>
6<\/td>D6.3: Text Mining and Natural Language Processing for Biomedical Data<\/td>Report<\/a><\/td>20<\/td><\/tr>
7<\/td>D7.1: Statistical Analysis Methodologies for GWAS Data \u2013 Technical Report<\/td>Report<\/a><\/td>28<\/td><\/tr>
7<\/td>D7.2: Data Clustering Techniques for GWAS Interpretation \u2013 Technical Report<\/td>Report<\/a><\/td>28<\/td><\/tr>
8<\/td>D8.1: Integrated Methodological Framework and Unified Data Sets \u2013 Technical Report<\/td>Report<\/a><\/td>28<\/td><\/tr>
8<\/td>D8.2: Advanced Deep Learning Methodologies \u2013 Technical Report<\/td>Report<\/a><\/td>28<\/td><\/tr>
9<\/td>D9.1: Identification and Characterization of VUS \u2013 Technical Report and International Conference Presentation<\/td>Other<\/a><\/td>28<\/td><\/tr>
9<\/td>D9.2: Evaluation of Deep Learning Models Using WGS Data from 30 Greek Patients \u2013 Technical Report<\/td>Report<\/a><\/td>28<\/td><\/tr>
10<\/td>D10.1: VUS Characterization and Results \u2013 Scientific Journal Publication<\/td>Other<\/a><\/td>28<\/td><\/tr>
10<\/td>D10.2: Functional Impact Assessment of Identified VUS \u2013 Technical Report<\/td>Report<\/a><\/td>28<\/td><\/tr>
11<\/td>D11.1: Knowledge Extraction from Biomedical Data \u2013 Technical Report<\/td>Report<\/a><\/td>28<\/td><\/tr>
11<\/td>D11.2: Delivery of Database and User Interface \u2013 Software and Journal Publication<\/td>Other<\/a><\/td>28<\/td><\/tr>
12<\/td>D12.1: Design and Development of an Intelligent Electronic Health Record Supporting Multi-Omic, Genetic, and Clinical Data<\/td>Software<\/a><\/td>28<\/td><\/tr>
12<\/td>D12.2: Development of a Smart Engine for Dynamic Creation of New Patient Registries<\/td>Software<\/a><\/td>28<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

<\/p>","protected":false},"excerpt":{"rendered":"

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