Author

E. Giampieri

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

Biology Disciplines

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Biophysics· 1 Cancer Biology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Assay:DNA methylation· 2 Organism:human· 2 Approach:biophysical modeling· 1 Assay:ChIP-seq· 1 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:clustering· 1 Organism:E. coli· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

C. Sala· 2 shared G. Castellani· 2 shared A. Merlotti· 1 shared A. T. Turki· 1 shared B. A. Pataki· 1 shared B. C. L. van der Putten· 1 shared C. Schultsz· 1 shared COMPARE ML-AMR group· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersCancer Biology

C. Sala

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Author profile →

3 papers

Recent·Most cited

Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in Onco-Hematology

D. Dall'Olio, E. Sträng, A. T. Turki, J. M. Tettero, M. Barbus, R. Schulze-Rath, J. M. Elicegui, T. Matteuzzi, A. Merlotti, L. Carota, C. Sala, M. G. Della Porta, E. Giampieri, J. M. Hernandez-Rivas, L. Bullinger, G. Castellani

This paper applies Hierarchical Dirichlet Mixture Models to binary genomic data in AML to improve patient stratification and clustering through statistical refinements.

bioRxiv · Jun 26, 2023Cancer Biology

Discovering footprints of evolutionary chromatin response to transposons activity: merging biophysics with bioinformatics

S. Vitali, E. Giampieri, C. Sala, I. Do Valle, S. Criscione, N. Neretti, G. Castellani

The paper models LINEs activity as an ecosystem to infer evolutionary chromatin responses in mammals and primates, finding contrasting patterns in murine and primate lineages.

bioRxiv · Feb 22, 2021 · PreprintBiophysics

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

B. A. Pataki, S. Matamoros, B. C. L. van der Putten, D. Remondini, E. Giampieri, D. Aytan-Aktug, R. S. Hendriksen, O. Lund, I. Csabai, C. Schultsz, COMPARE ML-AMR group

This paper predicts ciprofloxacin minimum inhibitory concentration in Escherichia coli using machine learning based on genome-wide mutation profiles and antimicrobial resistance gene presence.

bioRxiv · Jan 17, 2020Microbiology