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