M. van Baardwijk, G. Gavriilidis
M. van Baardwijk, G. Gavriilidis, N. Ishaque, O. Lazareva, V. Vasileiou, A. Orfanou, A. Stubbs, O. Stegle, and F. Psomopoulos, “Multitask perturbation modeling for single-cell omics,” Aug. 2024, doi: 10.7490/f1000research.1119837.1.
F. Psomopoulos, E. Capriotti,
F. Psomopoulos, E. Capriotti, N. Queralt-Rosinach, L. Jael Castro, and S. Tosatto, “Current activities of the ELIXIR Machine Learning Focus Group,” Sep. 2024, doi: 10.7490/f1000research.1119845.1.
V. Makarov, C. Chabber
V. Makarov, C. Chabbert, E. Koletou, F. Psomopoulos, N. Kurbatova, S. Ramirez, C. Nelson, P. Natarajan, and B. Neupane, “Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise,” Computers in Biology and Medicine, vol. 177, p. 108632, Jul. 2024, doi: 10.1016/j.compbiomed.2024.108632.
G. I. Gavriilidis, V. Vasileiou,
G. I. Gavriilidis, V. Vasileiou, A. Orfanou, N. Ishaque, and F. Psomopoulos, “A mini-review on perturbation modelling across single-cell omic modalities,” Computational and Structural Biotechnology Journal, vol. 23, pp. 1886–1896, Dec. 2024, doi: 10.1016/j.csbj.2024.04.058.
Tracking SARS-CoV-2
S. G. Sutcliffe et al., “Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data,” Microbial Genomics, vol. 10, no. 5, 2024, doi: https://doi.org/10.1099/mgen.0.001249.
N. Pechlivanis, G. Karakatsoulis,
N. Pechlivanis, G. Karakatsoulis, K. Kyritsis, M. Tsagiopoulou, S. Sgardelis, I. Kappas, and F. Psomopoulos, “Microbial co-occurrence network demonstrates spatial and climatic trends for global soil diversity,” Scientific Data, vol. 11, no. 1, p. 672, 2024, doi: 10.1038/s41597-024-03528-1.
S.-C. Fragkouli, D. Solanki
S.-C. Fragkouli, D. Solanki, L. Castro, F. Psomopoulos, N. Queralt-Rosinach, D. Cirillo, and L. Crossman, “Synthetic data: how could it be used in infectious disease research?,” Future Microbiology, vol. 0, no. 0, pp. 1–6, 2024, doi: 10.1080/17460913.2024.2400853.
DOME Registry: implementing community
O. A. Attafi et al., “DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology,” GigaScience, vol. 13, p. giae094, Dec. 2024, doi: 10.1093/gigascience/giae094.
Hilioti, Z., Antunes
Hilioti, Z., Antunes, D., Kalaitzis, P., & Merkouropoulos, G. (2024). Manipulation of plant architecture for crop production. Frontiers in Plant Science, 15, 1502833.
Reference 45 2024
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