Michele Guindani

3.4k total citations · 1 hit paper
104 papers, 2.0k citations indexed

About

Michele Guindani is a scholar working on Artificial Intelligence, Molecular Biology and Statistics and Probability. According to data from OpenAlex, Michele Guindani has authored 104 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 26 papers in Molecular Biology and 25 papers in Statistics and Probability. Recurrent topics in Michele Guindani's work include Bayesian Methods and Mixture Models (25 papers), Statistical Methods and Inference (16 papers) and Gene expression and cancer classification (13 papers). Michele Guindani is often cited by papers focused on Bayesian Methods and Mixture Models (25 papers), Statistical Methods and Inference (16 papers) and Gene expression and cancer classification (13 papers). Michele Guindani collaborates with scholars based in United States, Italy and United Kingdom. Michele Guindani's co-authors include Marina Vannucci, Alan E. Gelfand, Zhaoxia Yu, Lujia Chen, Todd C. Holmes, Xiangmin Xu, Steven F. Grieco, Jin‐Ao Duan, Brian J. Reich and Linlin Zhang and has published in prestigious journals such as Neuron, Nano Letters and Journal of the American Statistical Association.

In The Last Decade

Michele Guindani

92 papers receiving 2.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research

2021 272 citations Zhaoxia Yu, Michele Guindani et al. Neuron profile →

Peers

Michele Guindani

MB

AI

CN

SP

ONCOL

Erika Graf Germany

Sheng Luo United States

Ying Wei United States

Edna Schechtman Israel

Jian Kang United States

Fei Zou United States

Martin Däumer Germany

Jochen Kruppa Germany

Stefan Wellek Germany

Yves Lepage Canada

Erika Graf Germany

Michele Guindani

166 ×0.4

MB

168 ×0.4

AI

189 ×0.6

CN

352 ×1.2

SP

314 ×1.2

ONCOL

Citations per year, relative to Michele Guindani Michele Guindani (= 1×) peers Erika Graf

Countries citing papers authored by Michele Guindani

Since Specialization

Citations

This map shows the geographic impact of Michele Guindani's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michele Guindani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michele Guindani more than expected).

Fields of papers citing papers by Michele Guindani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michele Guindani. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michele Guindani. The network helps show where Michele Guindani may publish in the future.

Co-authorship network of co-authors of Michele Guindani

This figure shows the co-authorship network connecting the top 25 collaborators of Michele Guindani. A scholar is included among the top collaborators of Michele Guindani based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michele Guindani. Michele Guindani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Vannucci, Marina, et al.. (2025). Local Level Dynamic Random Partition Models for Changepoint Detection. Bayesian Analysis. -1(-1). 1 indexed citations

2.

Abdelhafez, Yasser G., Cyrus Bateni, Sonia Lee, et al.. (2025). MRI of atypical lipomatous tumor: does contrast help? A multicenter study. PubMed. 54(12). 2681–2693.

3.

Rossell, David, et al.. (2024). Semiparametric local variable selection under misspecification. Biometrika. 112(2).

4.

Telesca, Donatello, Michele Guindani, Catherine A. Sugar, et al.. (2024). Modeling intra‐individual inter‐trial EEG response variability in autism. Statistics in Medicine. 43(17). 3239–3263.

5.

Yu, Zhaoxia, Michele Guindani, Steven F. Grieco, et al.. (2021). Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron. 110(1). 21–35. 272 indexed citations breakdown →

6.

Chang, Shine, Michele Guindani, Page S. Morahan, et al.. (2020). Increasing Promotion of Women Faculty in Academic Medicine: Impact of National Career Development Programs. Journal of Women s Health. 29(6). 837–846. 29 indexed citations

7.

Guindani, Michele, et al.. (2020). A hierarchical bayesian model for differential connectivity in multi-trial brain signals. Econometrics and Statistics. 15. 117–135. 9 indexed citations

8.

Romero‐Olivares, Adriana L., et al.. (2020). A Bayesian approach to evaluation of soil biogeochemical models. Biogeosciences. 17(15). 4043–4057. 6 indexed citations

9.

Versace, Francesco, David W. Frank, Elise M. Stevens, et al.. (2018). The reality of “food porn”: Larger brain responses to food‐related cues than to erotic images predict cue‐induced eating. Psychophysiology. 56(4). e13309–e13309. 33 indexed citations

10.

Jara, Alejandro, et al.. (2018). A Bayesian semiparametric partially PH model for clustered time‐to‐event data. Scandinavian Journal of Statistics. 45(4). 1016–1035. 4 indexed citations

11.

Thall, Peter F., et al.. (2017). Bayesian nonparametric statistics: A new toolkit for discovery in cancer research. Pharmaceutical Statistics. 16(6). 414–423. 5 indexed citations

12.

Chiang, Sharon, Michele Guindani, Hsiang J. Yeh, et al.. (2017). A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection. Frontiers in Neuroscience. 11. 669–669. 6 indexed citations

13.

Liao, Zhongxing, A Melancon, Michele Guindani, et al.. (2015). Technical Note: A Monte Carlo study of magnetic‐field‐induced radiation dose effects in mice. Medical Physics. 42(9). 5510–5516. 13 indexed citations

14.

Airoldi, Edoardo M., et al.. (2014). Generalized Species Sampling Priors With Latent Beta Reinforcements. Journal of the American Statistical Association. 109(508). 1466–1480. 20 indexed citations

15.

Waters, Andrew E., et al.. (2014). A Bayesian nonparametric approach for the analysis of multiple categorical item responses. Journal of Statistical Planning and Inference. 166. 52–66. 3 indexed citations

16.

Zhang, Linlin, Michele Guindani, Francesco Versace, & Marina Vannucci. (2014). A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses. NeuroImage. 95. 162–175. 37 indexed citations

17.

Jung, Su Yon, Stephen D. Hursting, Michele Guindani, et al.. (2013). Bioavailable Insulin-like Growth Factor-I Inversely Related to Weight Gain in Postmenopausal Women Regardless of Exogenous Estrogen. Cancer Epidemiology Biomarkers & Prevention. 23(3). 534–544. 5 indexed citations

18.

Stingo, Francesco C., Michele Guindani, Marina Vannucci, & Vince D. Calhoun. (2013). An Integrative Bayesian Modeling Approach to Imaging Genetics. Journal of the American Statistical Association. 108(503). 876–891. 30 indexed citations

19.

Nardo, Lorenzo, Warapat Virayavanich, Linlin Zhang, et al.. (2013). Bone marrow changes related to disuse. European Radiology. 23(12). 3422–3431. 6 indexed citations

20.

Guindani, Michele, et al.. (2013). A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles. Computational Mechanics. 53(3). 539–547. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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