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Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.
Published in:
Scientific Reports, 2022, v. 12, n. 1, p. 1, doi. 10.1038/s41598-022-12364-5
By:
- D'Orazio, M.;
- Murdocca, M.;
- Mencattini, A.;
- Casti, P.;
- Filippi, J.;
- Antonelli, G.;
- Di Giuseppe, D.;
- Comes, M. C.;
- Di Natale, C.;
- Sangiuolo, F.;
- Martinelli, E.
Publication type:
Article
Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.
Published in:
Scientific Reports, 2020, v. 10, n. 1, p. 1, doi. 10.1038/s41598-020-64246-3
By:
- Mencattini, A.;
- Di Giuseppe, D.;
- Comes, M. C.;
- Casti, P.;
- Corsi, F.;
- Bertani, F. R.;
- Ghibelli, L.;
- Businaro, L.;
- Di Natale, C.;
- Parrini, M. C.;
- Martinelli, E.
Publication type:
Article
The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: a systematic study for time-lapse microscopy applications.
Published in:
Scientific Reports, 2019, v. 9, n. 1, p. N.PAG, doi. 10.1038/s41598-019-42475-5
By:
- Comes, M. C.;
- Casti, P.;
- Mencattini, A.;
- Di Giuseppe, D.;
- Mermet-Meillon, F.;
- De Ninno, A.;
- Parrini, M. C.;
- Businaro, L.;
- Di Natale, C.;
- Martinelli, E.
Publication type:
Article
Multi-scale generative adversarial network for improved evaluation of cell–cell interactions observed in organ-on-chip experiments.
Published in:
Neural Computing & Applications, 2021, v. 33, n. 8, p. 3671, doi. 10.1007/s00521-020-05226-6
By:
- Comes, M. C.;
- Filippi, J.;
- Mencattini, A.;
- Casti, P.;
- Cerrato, G.;
- Sauvat, A.;
- Vacchelli, E.;
- De Ninno, A.;
- Di Giuseppe, D.;
- D'Orazio, M.;
- Mattei, F.;
- Schiavoni, G.;
- Businaro, L.;
- Di Natale, C.;
- Kroemer, G.;
- Martinelli, E.
Publication type:
Article
Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis.
Published in:
Communications Biology, 2023, v. 6, n. 1, p. 1, doi. 10.1038/s42003-023-04585-9
By:
- Mencattini, A.;
- D'Orazio, M.;
- Casti, P.;
- Comes, M. C.;
- Di Giuseppe, D.;
- Antonelli, G.;
- Filippi, J.;
- Corsi, F.;
- Ghibelli, L.;
- Veith, I.;
- Di Natale, C.;
- Parrini, M. C.;
- Martinelli, E.
Publication type:
Article

Found: 5

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: a systematic study for time-lapse microscopy applications.

Multi-scale generative adversarial network for improved evaluation of cell–cell interactions observed in organ-on-chip experiments.

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis.