Author

Roberto Molinari

Auburn UniversityORCID 0000-0001-8766-6902Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Organism:Arabidopsis· 1 Research Focus:gene regulatory network inference· 1 Research Focus:host-pathogen & immune response· 1 Organism:human· 1

Frequent Co-Authors

Stéphane GuerrieriD· 2 shared Amanpreet KauriD· 1 shared Amy Marshall‐ColóniD· 1 shared Anthony P. KeinathiD· 1 shared Bhabesh DuttaiD· 1 shared Cesare MiglioliiD· 1 shared Destiny Brokaw· 1 shared Edward J. SikoraiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersSystems Biology

Stéphane GuerrierORCID

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3 papers

Recent·Most cited

Interaction between climatic variation and pathogen diversity shape endemic disease dynamics in the agricultural settings

Rishi Bhandari, Amanpreet Kaur, Ivory Russell, Ogonnaya Michael Romanus, Destiny Brokaw, Anthony P. Keinath, Zachary Snipes, Philip Rollins, Nettie Baugher, Inga M. Meadows, T. N. Torrance, Bhabesh Dutta, Edward J. Sikora, Roberto Molinari, Samuel Soubeyrand, Neha Potnis

This study shows how pathogen genetic diversity and climatic variation interact to influence disease outbreaks in agricultural settings using high-resolution sequencing and analysis.

Molecular Ecology · Jul 17, 2025Peer-reviewedMicrobiology

Granger-Causal Testing for Irregularly Sampled Time Series with Application to Nitrogen Signaling in Arabidopsis

Sachin Heerah, Roberto Molinari, Stéphane Guerrier, Amy Marshall‐Colón

This paper develops a statistical framework for identifying Granger causal relationships in irregularly sampled time series data from Arabidopsis thaliana, revealing 3,078 significant gene interactions related to nitrogen signaling.

Bioinformatics · Mar 4, 2021Peer-reviewedSystems Biology

Chameleon microRNAs in breast cancer: their elusive role as regulatory factors in cancer progression

Cesare Miglioli, Gaetan Bakalli, Samuel Orso, Mucyo Karemera, Roberto Molinari, Stéphane Guerrier, Nabil Mili

The paper identifies 112 miRNA combinations that accurately distinguish healthy breast tissue from breast carcinoma using a machine learning approach that generates sets of equivalent predictive models.

bioRxiv · Dec 15, 2020 · PreprintSystems Biology