Genomics and Personalized Medicine for all though Artificial Intelligence in Haematological Diseases

Leistungen

Data processing, data management plan and GDPR compliance report

This deliverable will specify the intended data sources data flows and data processing across the consortium and its work plan to form a complete Data Management Plan It will confirm the completion of a DPIA at each relevant partner site and summarise the main areas of risk and mitigation identified It will confirm the legal basis for the processing of each data source noting these will all be special category data and will include copies of approved consent forms in cases where consent is the legal basis

Data homogenisation requirements and specifications

This report will define the data homogenisation processes to be included in GENOMED4ALL

Literature mining and preprocessing.

Literature mining and first AI software release data preprocessing and statistical data cleaning All the software will be open source R Python and C and will be made available for the other project partners

Report 1 of the EAB

This will be the first report of the EAB Including its terms of reference and constitution plus its initial appraisal of the intended data protection human ethics and artificial intelligence approaches

GENOMED4ALL standardization plan

This deliverable will recommend adapt and calibrate data standards for data collected from different clinical partners

GENOMED4ALL Impact Master Plan

A document outlining the project dissemination communication exploitation strategies and detailing the project plan of concrete GENOMED4ALL activities including project website

Preliminary conclusions about federated learning applied to clinical data.

Federated learning software release and report

Hybrid Federated learning model.

This deliverable will describe the AI learning mechanisms to make sure a hybrid approach using federated and centralised learning scheme can be applied to GENOMED4ALL

Veröffentlichungen

Covering hierarchical Dirichlet mixture models on binary data to enhance genomic stratifications in onco-hematology

Autoren: D. D. Olio, E. Sträng, A. T. Turki, J. M. Tettero, M. Barbus, R. Schulze-Rath, J. Martinez, T. Matteuzzi, A. Merlotti, L. Carota, C. Sala, M. G. D. Porta et al.
Veröffentlicht in: PLOS Computational Biology, Ausgabe Volume 20, 2023, Seite(n) e1011299, ISSN 1553-7358
Herausgeber: PLOS
DOI: 10.1101/2023.06.26.546639

DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine

Autoren: M. Bastico; A. Fernández-García; A. Belmonte-Hernández; S. Uribe et al.
Veröffentlicht in: IEEE Access, Ausgabe Volume 11, 2023, Seite(n) 37378-37391, ISSN 2169-3536
Herausgeber: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2023.3266983

Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology

Autoren: S. D. Amico, D. D. Olio , C. Sala, L. D. Olio, E. Sauta, M. Zampini et al.
Veröffentlicht in: JCO Clinical Cancer Informatics, Ausgabe Volume 7, 2023, ISSN 2473-4276
Herausgeber: ASCO publications
DOI: 10.1200/cci.23.00021

Challenges and Opportunities of Precision Medicine in Sickle Cell Disease: Novel European Approach by GenoMed4All Consortium and ERN-EuroBloodNet

Autoren: A. Collado, M. P. Boaro, S. van der Veen, A. Idrizovic, B. J. Biemond, D. Beneitez Pastor, A. Ortuño, E. Cela, A. Ruiz-Llobet, P. Bartolucci, M. de Montalembert et al.
Veröffentlicht in: HemaSphere, Ausgabe 7, 2023, Seite(n) e844-e846, ISSN 2572-9241
Herausgeber: Wiley
DOI: 10.1097/hs9.0000000000000844

Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization

Autoren: L. Squadrani, N. Curti, E. Giampieri, D. Remondini, B. Blais, G.Castellani
Veröffentlicht in: Entropy, Ausgabe 24, 2022, ISSN 1099-4300
Herausgeber: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/e24050682

Random walk approximation for stochastic processes on graphs

Autoren: S. Polizzi, T. Marzi, T. Matteuzzi, G. Castellani, A. Bazzani
Veröffentlicht in: Entropy, Ausgabe 25, 2023, ISSN 1099-4300
Herausgeber: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/e25030394

Real-world Validation of Molecular International Prognostic Scoring System (IPSS-M) for Myelodysplastic Syndromes

Autoren: E. Sauta, M. Robin, M. Bersanelli, E. Travaglino, M. Meggendorfer, L. Zhao et al.
Veröffentlicht in: Journal of Clinical Oncology, Ausgabe 41, 2023, ISSN 1527-7755
Herausgeber: Asco Publicationes
DOI: 10.1200/jco.22.01784

Synthetic Histopathological Images Generation with Artificial Intelligence to Accelerate Research and Improve Clinical Outcomes in Hematology

Autoren: G. Asti, S. D'Amico, N. Curti, G. Carlini, E. Sauta, N. R. Derus, D. Dall'Olio et al.
Veröffentlicht in: 65th ASH Annual Meeting Proceedings, 2023
Herausgeber: ASH Publications
DOI: 10.1182/blood-2023-187521

Data-Driven Harmonization of 2022 Who and ICC Classifications of Myelodysplastic Syndromes/Neoplasms (MDS): A Study By the International Consortium for MDS (icMDS)

Autoren: L. Lanino, S. Ball, J. P. Bewersdorf, M. Marchetti, G. Maggioni, E. Travaglino, N. H. A. Ali, P. Fenaux, U. Platzbecker, V. Santini, M. Diez-Campelo et al.
Veröffentlicht in: 65th ASH Annual Meeting Proceedings, 2023
Herausgeber: ASH Publications
DOI: 10.1182/blood-2023-186580

Combining Gene Mutation with Transcriptomic Data Improves Outcome Prediction in Myelodysplastic Syndromes

Autoren: Combining Gene Mutation with Transcriptomic Data Improves Outcome Prediction in Myelodysplastic Syndromes
Veröffentlicht in: 65th ASH Annual Meeting Proceedings, 2023
Herausgeber: ASH Publications
DOI: 10.1182/blood-2023-186222

Leistungen

Veröffentlichungen

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