Matteo Togninalli

Biochemistry, Genetics and Molecular BiologyComputer ScienceAgricultural and Biological Sciences

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 2 papers. Top paper: “Machine learning-based classification of binary dynamic fluorescence signals reveals muscle stem cell fate transitions in response to pro-regenerative niche factors”.

Machine learning-based classification of binary dynamic fluorescence signals reveals muscle stem cell fate transitions in response to pro-regenerative niche factors

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.06.17.495556

N/A

0DataRank · unranked

1 citations

Andrew Tri Van Ho, Christopher M. Madl, Colin Holbrook, Yu Xin Wang, Klas E. G. Magnusson +4 more

Machine learning-based classification of dual fluorescence signals reveals muscle stem cell fate transitions in response to regenerative niche factors

npj Regenerative Medicine(2023)10.1038/s41536-023-00277-4

N/A

0DataRank · unranked

8 citations

Andrew Tri Van Ho, Christopher M. Madl, Colin Holbrook, Yu Xin Wang, Klas E. G. Magnusson +4 more