Synthetic data: how could it be used in infectious disease research?

Fragkouli, Styliani-Christina; Solanki, Dhwani; Castro, Leyla J; Psomopoulos, Fotis E; Queralt-Rosinach, Núria; Cirillo, Davide; Crossman, Lisa C

Fragkouli, Styliani-Christina; Solanki, Dhwani; Castro, Leyla J; Psomopoulos, Fotis E; Queralt-Rosinach, Núria; Cirillo, Davide; Crossman, Lisa C.

Affiliation

Fragkouli SC; Department of Biology, National & Kapodistrian University of Athens, Athens, 15772, Greece.
Solanki D; Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, 57001, Greece.
Castro LJ; ZB MED Information Centre for Life Sciences, Cologne, 50931, Germany.
Psomopoulos FE; ZB MED Information Centre for Life Sciences, Cologne, 50931, Germany.
Queralt-Rosinach N; Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, 57001, Greece.
Cirillo D; Department of Human Genetics, Leiden University Medical Center, Leiden, 2333, The Netherlands.
Crossman LC; Barcelona Supercomputing Center (BSC), Barcelona, E-08034, Spain.

Future Microbiol ; : 1-6, 2024 Sep 30.

Article in En | MEDLINE | ID: mdl-39345126

Key words

bacteria; deep learning; healthcare; infection; infectious disease; machine learning; microbiology; synthetic data

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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Future Microbiol Journal subject: MICROBIOLOGIA Year: 2024 Document type: Article Affiliation country: Greece Country of publication: United kingdom

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