Regina Berretta

2.1k total citations
59 papers, 1.1k citations indexed

About

Regina Berretta is a scholar working on Molecular Biology, Artificial Intelligence and Industrial and Manufacturing Engineering. According to data from OpenAlex, Regina Berretta has authored 59 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 16 papers in Artificial Intelligence and 9 papers in Industrial and Manufacturing Engineering. Recurrent topics in Regina Berretta's work include Gene expression and cancer classification (18 papers), Bioinformatics and Genomic Networks (17 papers) and Supply Chain and Inventory Management (8 papers). Regina Berretta is often cited by papers focused on Gene expression and cancer classification (18 papers), Bioinformatics and Genomic Networks (17 papers) and Supply Chain and Inventory Management (8 papers). Regina Berretta collaborates with scholars based in Australia, Brazil and Chile. Regina Berretta's co-authors include Pablo Moscato, Carlos Riveros, Alexandre Mendes, Martı́n Gómez Ravetti, Osvaldo A. Rosso, Daniel M. Johnstone, Nasimul Noman, Inna Tishchenko, Heloisa Milioli and Mohammad Nazmul Haque and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and International Journal of Production Economics.

In The Last Decade

Regina Berretta

56 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Regina Berretta Australia 20 412 226 170 168 116 59 1.1k
Sai Peck Lee Malaysia 32 652 1.6× 303 1.3× 66 0.4× 379 2.3× 92 0.8× 169 3.2k
Panpan Xu China 22 456 1.1× 376 1.7× 72 0.4× 113 0.7× 160 1.4× 67 1.7k
Zhang Qishan China 16 239 0.6× 226 1.0× 56 0.3× 97 0.6× 39 0.3× 153 1.1k
Shufen Liu China 19 381 0.9× 122 0.5× 28 0.2× 66 0.4× 69 0.6× 146 1.1k
Xiaofeng Wang China 20 219 0.5× 117 0.5× 34 0.2× 35 0.2× 54 0.5× 76 1.6k
Rebecka Jörnsten Sweden 19 753 1.8× 127 0.6× 90 0.5× 78 0.5× 238 2.1× 44 1.4k
Wenyu Yu China 22 595 1.4× 324 1.4× 56 0.3× 32 0.2× 35 0.3× 57 1.9k
Yong Xie China 18 90 0.2× 156 0.7× 86 0.5× 48 0.3× 21 0.2× 78 1.1k
Martı́n Gómez Ravetti Brazil 20 199 0.5× 147 0.7× 488 2.9× 103 0.6× 7 0.1× 51 1.2k
Sultan Ahmad Saudi Arabia 25 726 1.8× 417 1.8× 13 0.1× 321 1.9× 31 0.3× 153 2.4k

Countries citing papers authored by Regina Berretta

Since Specialization
Citations

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

Fields of papers citing papers by Regina Berretta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Regina Berretta

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

All Works

20 of 20 papers shown
1.
Lane, Cassandra, et al.. (2024). Does targeted recruitment turn the dial for gender equity? A qualitative study at an Australian University. Higher Education Quarterly. 78(3). 934–956.
2.
Milioli, Heloisa, Inna Tishchenko, Carlos Riveros, Regina Berretta, & Pablo Moscato. (2017). Basal-like breast cancer: molecular profiles, clinical features and survival outcomes. BMC Medical Genomics. 10(1). 19–19. 73 indexed citations
3.
Milioli, Heloisa, et al.. (2016). Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset. BioData Mining. 9(1). 2–2. 15 indexed citations
4.
Berretta, Regina, et al.. (2015). FSMEC: a feature selection method based on the minimum spanning tree and evolutionary computation. 129–139. 2 indexed citations
5.
Riveros, Carlos, et al.. (2015). A New Combinatorial Optimization Approach for Integrated Feature Selection Using Different Datasets: A Prostate Cancer Transcriptomic Study. PLoS ONE. 10(6). e0127702–e0127702. 7 indexed citations
6.
Milioli, Heloisa, et al.. (2015). The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set. PLoS ONE. 10(7). e0129711–e0129711. 19 indexed citations
7.
Riveros, Carlos, et al.. (2012). GPU-FS-kNN: A Software Tool for Fast and Scalable kNN Computation Using GPUs. PLoS ONE. 7(8). e44000–e44000. 44 indexed citations
8.
9.
Mathieson, Luke, et al.. (2012). Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression. PLoS ONE. 7(9). e45535–e45535. 27 indexed citations
10.
Ravetti, Martı́n Gómez, et al.. (2011). Differences in Abundances of Cell-Signalling Proteins in Blood Reveal Novel Biomarkers for Early Detection Of Clinical Alzheimer's Disease. PLoS ONE. 6(3). e17481–e17481. 24 indexed citations
11.
Berretta, Regina & Pablo Moscato. (2010). Cancer Biomarker Discovery: The Entropic Hallmark. PLoS ONE. 5(8). e12262–e12262. 35 indexed citations
12.
Riveros, Carlos, Kaushal Gandhi, Fiona C. McKay, et al.. (2010). A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis. PLoS ONE. 5(12). e14176–e14176. 46 indexed citations
13.
Ravetti, Martı́n Gómez, Osvaldo A. Rosso, Regina Berretta, & Pablo Moscato. (2010). Uncovering Molecular Biomarkers That Correlate Cognitive Decline with the Changes of Hippocampus' Gene Expression Profiles in Alzheimer's Disease. PLoS ONE. 5(4). e10153–e10153. 112 indexed citations
14.
Rosso, Osvaldo A., Alexandre Mendes, Regina Berretta, et al.. (2009). Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity (II): A combinatorial optimization approach for electrode selection. Journal of Neuroscience Methods. 181(2). 257–267. 17 indexed citations
15.
Berretta, Regina, et al.. (2008). Combinatorial Optimization Models for Finding Genetic Signatures from Gene Expression Datasets. Methods in molecular biology. 453. 363–377. 26 indexed citations
16.
Berretta, Regina, Alexandre Mendes, & Pablo Moscato. (2007). Selection of Discriminative Genes in Microarray Experiments Using Mathematical Programming. NOVA (University of Newcastle Australia). 14 indexed citations
17.
Moscato, Pablo, Alexandre Mendes, & Regina Berretta. (2006). Benchmarking a memetic algorithm for ordering microarray data. Biosystems. 88(1-2). 56–75. 54 indexed citations
18.
Berretta, Regina, Carlos Cotta, & Pablo Moscato. (2001). Forma Analysis and new Heuristic Ideas for the Number Partitioning Problem.
19.
Berretta, Regina & Pablo Moscato. (1999). The number partitioning problem: an open challenge for evolutionary computation?. 28(24). 261–278. 24 indexed citations
20.
França, Paulo Morelato, Vinı́cius Amaral Armentano, Regina Berretta, & Alistair Clark. (1997). A Heuristic For Lot-Sizing In Multi-Stage Systems. UWE Research Repository (UWE Bristol). 7 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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