Author

F. Zamberlan

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Neuroscience· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Organism:human· 1 Assay:proteomics· 1

Frequent Co-Authors

C. Pallavicini· 2 shared E. Tagliazucchi· 2 shared F. Carrillo· 2 shared L. A. de la Fuente· 2 shared A. M. Garcia· 1 shared A. Sanchez Ferran· 1 shared C. Sanz· 1 shared F. Cavanna· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersPharmacology & Toxicology

C. Pallavicini

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Natural language signatures of psilocybin microdosing

C. Sanz, F. Cavanna, S. Muller, L. A. de la Fuente, F. Zamberlan, M. Palmucci, L. Janeckova, M. Kuchar, F. Carrillo, A. M. Garcia, C. Pallavicini, E. Tagliazucchi

This study uses natural language analysis to detect psilocybin microdosing effects, finding significant differences in speech metrics between active and placebo conditions.

bioRxiv · Feb 20, 2022Pharmacology & Toxicology

Over eight hundred cannabis strains characterized by the relationship between their psychoactive effects, perceptual profiles, and chemical compositions

L. A. de la Fuente, F. Zamberlan, A. Sanchez Ferran, F. Carrillo, E. Tagliazucchi, C. Pallavicini

The paper characterizes over 800 cannabis strains by linking psychoactive effects, perceptual profiles, and chemical compositions using machine learning and user-reported data.

bioRxiv · Sep 8, 2019 · PreprintNeuroscience