Marco Muselli

2.6k total citations · 1 hit paper
77 papers, 1.5k citations indexed

About

Marco Muselli is a scholar working on Artificial Intelligence, Molecular Biology and Control and Systems Engineering. According to data from OpenAlex, Marco Muselli has authored 77 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 16 papers in Molecular Biology and 10 papers in Control and Systems Engineering. Recurrent topics in Marco Muselli's work include Neural Networks and Applications (13 papers), Gene expression and cancer classification (10 papers) and Face and Expression Recognition (7 papers). Marco Muselli is often cited by papers focused on Neural Networks and Applications (13 papers), Gene expression and cancer classification (10 papers) and Face and Expression Recognition (7 papers). Marco Muselli collaborates with scholars based in Italy, United States and Lebanon. Marco Muselli's co-authors include Diego Liberati, Giancarlo Ferrari‐Trecate, Manfred Morari, Enrico Ferrari, Cristiano Cervellera, Stefano Parodi, Giorgio Valentini, Paolo Bartolini, Elena Carcano and Luigi Piroddi and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Clinical Endocrinology & Metabolism and Analytical Biochemistry.

In The Last Decade

Marco Muselli

74 papers receiving 1.5k 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.

A clustering technique for the identification of piecewise affine systems

2003 426 citations Marco Muselli et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Marco Muselli Italy	22	513	373	229	115	93	77	1.5k
Xiaoxiong Liu China	21	222 0.4×	291 0.8×	374 1.6×	56 0.5×	160 1.7×	141	1.5k
Ning Li China	31	749 1.5×	336 0.9×	156 0.7×	732 6.4×	67 0.7×	261	2.8k
Mian Li China	26	243 0.5×	103 0.3×	197 0.9×	447 3.9×	129 1.4×	140	2.3k
Mario Luca Fravolini Italy	27	758 1.5×	376 1.0×	36 0.2×	133 1.2×	440 4.7×	163	2.2k
Zhijing Yang China	21	222 0.4×	350 0.9×	74 0.3×	107 0.9×	559 6.0×	126	1.6k
Ingrid K. Glad Norway	19	112 0.2×	185 0.5×	354 1.5×	40 0.3×	96 1.0×	46	1.2k
Renchu Guan China	24	125 0.2×	823 2.2×	256 1.1×	83 0.7×	357 3.8×	104	1.8k
Qasem Al-Tashi Malaysia	16	263 0.5×	946 2.5×	97 0.4×	311 2.7×	322 3.5×	39	2.1k
Ke Chen China	25	311 0.6×	1.2k 3.2×	279 1.2×	263 2.3×	249 2.7×	92	2.3k

Countries citing papers authored by Marco Muselli

Since Specialization

Citations

This map shows the geographic impact of Marco Muselli'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 Marco Muselli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Muselli more than expected).

Fields of papers citing papers by Marco Muselli

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco Muselli. 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 Marco Muselli. The network helps show where Marco Muselli may publish in the future.

Co-authorship network of co-authors of Marco Muselli

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Muselli. A scholar is included among the top collaborators of Marco Muselli 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 Marco Muselli. Marco Muselli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Dabbene, Fabrizio, et al.. (2025). A novel score function for conformal prediction in rule-based binary classification. Pattern Recognition. 171. 112219–112219.

Baccetti, Fabio, Davide Masi, Nicoletta Musacchio, et al.. (2025). Treatment Intensification Strategies and Metabolic Outcomes in Individuals With Type 2 Diabetes on GLP-1 RA Therapy. The Journal of Clinical Endocrinology & Metabolism. 110(12). e4101–e4110.

Masi, Davide, Fabio Baccetti, Besmir Nreu, et al.. (2024). Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control. SHILAP Revista de lepidopterología. 6(1). 420–434. 1 indexed citations

Musacchio, Nicoletta, Davide Masi, Fabio Baccetti, et al.. (2024). A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy. International Journal of Medical Informatics. 190. 105550–105550. 1 indexed citations

Masi, Davide, Riccardo Candido, A. Giancaterini, et al.. (2023). Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group. Journal of Clinical Medicine. 12(12). 4095–4095. 7 indexed citations

Muselli, Marco, et al.. (2023). Trustworthy artificial intelligence classification-based equivalent bandwidth control. Computer Communications. 209. 260–272. 1 indexed citations

Ferrari, Enrico, et al.. (2023). Optimizing Water Distribution through Explainable AI and Rule-Based Control. Computers. 12(6). 123–123. 8 indexed citations

Giorda, Carlo, Antonio Rossi, Fabio Baccetti, et al.. (2023). Achieving Good Metabolic Control Without Weight Gain with the Systematic Use of GLP-1-RAs and SGLT-2 Inhibitors in Type 2 Diabetes: A Machine-learning Projection Using Data from Clinical Practice. Clinical Therapeutics. 45(8). 754–761. 4 indexed citations

Daher, Ahmad, et al.. (2021). Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge. Sensors. 21(19). 6526–6526. 7 indexed citations

10.

Daher, Ahmad, et al.. (2020). Pedestrian and Multi-Class Vehicle Classification in Radar Systems Using Rulex Software on the Raspberry Pi. Applied Sciences. 10(24). 9113–9113. 4 indexed citations

11.

Parodi, Stefano, et al.. (2019). Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods. BMC Bioinformatics. 20(S9). 390–390. 12 indexed citations

12.

Parodi, Stefano, et al.. (2017). Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine. Journal of Gambling Studies. 33(4). 1121–1137. 5 indexed citations

13.

Mordenti, Marina, Enrico Ferrari, Elena Pedrini, et al.. (2013). Validation of a new multiple osteochondromas classification through Switching Neural Networks. American Journal of Medical Genetics Part A. 161(3). 556–560. 30 indexed citations

14.

Mangerini, Rosa, Paolo Romano, Angelo Facchiano, et al.. (2011). The application of atmospheric pressure matrix-assisted laser desorption/ionization to the analysis of long-term cryopreserved serum peptidome. Analytical Biochemistry. 417(2). 174–181. 18 indexed citations

15.

Cervellera, Cristiano, Danilo Macciò, & Marco Muselli. (2010). Efficient global maximum likelihood estimation through kernel methods. Neural Networks. 23(7). 917–925. 4 indexed citations

16.

Muselli, Marco, et al.. (2008). Evaluating switching neural networks through artificial and real gene expression data. Artificial Intelligence in Medicine. 45(2-3). 163–171. 8 indexed citations

17.

Muselli, Marco, et al.. (2005). A machine learning approach to estimate frequency, duration & availability indexes in complex networks. 250–255. 3 indexed citations

18.

Parodi, Stefano, Marco Muselli, V. Fontana, & Stefano Bonassi. (2003). ROC curves are a suitable and flexible tool for the analysis of gene expression profiles. Cytogenetic and Genome Research. 101(1). 90–91. 17 indexed citations

19.

Milanesi, Luciano, Marco Muselli, & Patrizio Arrigo. (1996). Hamming-Clustering method for signals prediction in 5′ and 3′ regions of eukaryotic genes. Computer applications in the biosciences. 12(5). 399–404. 45 indexed citations

20.

Muselli, Marco & Sandro Ridella. (1992). Global Optimization of Functions with the Interval Genetic Algorithm.. Complex Systems. 6. 9 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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