Fábio Fabris

1.2k total citations
24 papers, 424 citations indexed

About

Fábio Fabris is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Fábio Fabris has authored 24 papers receiving a total of 424 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 8 papers in Artificial Intelligence and 6 papers in Computational Theory and Mathematics. Recurrent topics in Fábio Fabris's work include Bioinformatics and Genomic Networks (9 papers), Machine Learning in Bioinformatics (6 papers) and Gene expression and cancer classification (6 papers). Fábio Fabris is often cited by papers focused on Bioinformatics and Genomic Networks (9 papers), Machine Learning in Bioinformatics (6 papers) and Gene expression and cancer classification (6 papers). Fábio Fabris collaborates with scholars based in United Kingdom, Brazil and Italy. Fábio Fabris's co-authors include Alex A. Freitas, João Pedro de Magalhães, Daniel H. Palmer, Aoife Doherty, Renato A. Krohling, Ester Marotta, Pietro Traldi, Roberta Seraglia, Flávio Miguel Varejão and Thomas W. Rauber and has published in prestigious journals such as Bioinformatics, Expert Systems with Applications and Neurocomputing.

In The Last Decade

Fábio Fabris

23 papers receiving 409 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fábio Fabris United Kingdom 11 151 93 49 45 41 24 424
Javier González United Kingdom 12 176 1.2× 184 2.0× 8 0.2× 89 2.0× 98 2.4× 33 520
Thomas A. Geddes Australia 7 242 1.6× 83 0.9× 20 0.4× 3 0.1× 21 0.5× 10 467
Qingyong Wang China 13 67 0.4× 115 1.2× 11 0.2× 2 0.0× 7 0.2× 44 442
Pritam Chanda United States 13 217 1.4× 36 0.4× 4 0.1× 4 0.1× 31 0.8× 23 546
Fei He United Kingdom 16 167 1.1× 71 0.8× 3 0.1× 2 0.0× 33 0.8× 84 754
Jiancheng Zhong China 13 540 3.6× 36 0.4× 13 0.3× 3 0.1× 203 5.0× 27 723
András Kocsor Hungary 12 153 1.0× 327 3.5× 17 0.3× 1 0.0× 38 0.9× 47 609
S. Usui Japan 10 54 0.4× 144 1.5× 4 0.1× 2 0.0× 14 0.3× 45 366
Daisuke Tominaga Japan 12 559 3.7× 186 2.0× 10 0.2× 54 1.3× 35 814

Countries citing papers authored by Fábio Fabris

Since Specialization
Citations

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

Fields of papers citing papers by Fábio Fabris

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fábio Fabris

This figure shows the co-authorship network connecting the top 25 collaborators of Fábio Fabris. A scholar is included among the top collaborators of Fábio Fabris 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 Fábio Fabris. Fábio Fabris 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.
Palmer, Daniel H., Fábio Fabris, Aoife Doherty, Alex A. Freitas, & João Pedro de Magalhães. (2021). Ageing transcriptome meta-analysis reveals similarities and differences between key mammalian tissues. Aging. 13(3). 3313–3341. 48 indexed citations
2.
Plastino, Alexandre, et al.. (2020). A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti-Longevity Genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(6). 2230–2238. 10 indexed citations
3.
Fabris, Fábio, Daniel H. Palmer, João Pedro de Magalhães, & Alex A. Freitas. (2019). Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes. Briefings in Bioinformatics. 21(3). 803–814. 15 indexed citations
4.
Fabris, Fábio, et al.. (2018). Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem. Kent Academic Repository (University of Kent). 169–174. 6 indexed citations
5.
Fabris, Fábio, Aoife Doherty, Daniel H. Palmer, João Pedro de Magalhães, & Alex A. Freitas. (2018). A new approach for interpreting Random Forest models and its application to the biology of ageing. Bioinformatics. 34(14). 2449–2456. 50 indexed citations
6.
Fabris, Fábio, João Pedro de Magalhães, & Alex A. Freitas. (2017). A review of supervised machine learning applied to ageing research. Biogerontology. 18(2). 171–188. 83 indexed citations
7.
Plastino, Alexandre, et al.. (2017). A novel probabilistic Jaccard distance measure for classification of sparse and uncertain data. Kent Academic Repository (University of Kent). 5 indexed citations
8.
Fabris, Fábio & Alex A. Freitas. (2016). New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins. Bioinformatics. 32(19). 2988–2995. 8 indexed citations
9.
Johnson, Colin G., et al.. (2016). A Situation-Aware Fear Learning (SAFEL) model for robots. Neurocomputing. 221. 32–47. 21 indexed citations
10.
Fabris, Fábio, Alex A. Freitas, & Jennifer M. A. Tullet. (2015). An Extensive Empirical Comparison of Probabilistic Hierarchical Classifiers in Datasets of Ageing-Related Genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 13(6). 1045–1058. 11 indexed citations
11.
Fabris, Fábio & Alex A. Freitas. (2015). A Novel Extended Hierarchical Dependence Network Method Based on Non-hierarchical Predictive Classes and Applications to Ageing-Related Data. Kent Academic Repository (University of Kent). 1. 294–301. 1 indexed citations
12.
Fabris, Fábio & Alex A. Freitas. (2014). An Efficient Algorithm for Hierarchical Classification of Protein and Gene Functions. Kent Academic Repository (University of Kent). 7. 64–68. 2 indexed citations
13.
Rauber, Thomas W., et al.. (2013). Automatic diagnosis of submersible motor pump conditions in offshore oil exploration. 5537–5542. 7 indexed citations
14.
Fabris, Fábio, et al.. (2013). Using GA for the stratified sampling of electricity consumers. 220. 261–268.
15.
Fabris, Fábio, et al.. (2013). Optimization metaheuristics for minimizing variance in a real-world statistical application. 206–207. 2 indexed citations
16.
Fabris, Fábio & Renato A. Krohling. (2011). A co-evolutionary differential evolution algorithm for solving min–max optimization problems implemented on GPU using C-CUDA. Expert Systems with Applications. 39(12). 10324–10333. 28 indexed citations
17.
Fabris, Fábio, et al.. (2010). Constructing feature-based ensemble classifiers for real-world machines fault diagnosis. Kent Academic Repository (University of Kent). 1099–1104. 1 indexed citations
18.
Fabris, Fábio, et al.. (2010). A Comparison of Two Feature-Based Ensemble Methods for Constructing Motor Pump Fault Diagnosis Classifiers. Kent Academic Repository (University of Kent). 417–420. 1 indexed citations
19.
Fabris, Fábio, et al.. (2009). Novel Approaches for Detecting Frauds in Energy Consumption. Kent Academic Repository (University of Kent). 1. 546–551. 5 indexed citations
20.
Fabris, Fábio, et al.. (2008). JoinUs: Management of Mobile Social Networks for Pervasive Collaboration. Kent Academic Repository (University of Kent). 224–234. 3 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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