Daniela Stojanova

461 total citations
10 papers, 322 citations indexed

About

Daniela Stojanova is a scholar working on Molecular Biology, Information Systems and Artificial Intelligence. According to data from OpenAlex, Daniela Stojanova has authored 10 papers receiving a total of 322 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Molecular Biology, 2 papers in Information Systems and 2 papers in Artificial Intelligence. Recurrent topics in Daniela Stojanova's work include Data Mining Algorithms and Applications (2 papers), Advanced Clustering Algorithms Research (2 papers) and Membrane Separation and Gas Transport (1 paper). Daniela Stojanova is often cited by papers focused on Data Mining Algorithms and Applications (2 papers), Advanced Clustering Algorithms Research (2 papers) and Membrane Separation and Gas Transport (1 paper). Daniela Stojanova collaborates with scholars based in Slovenia, Italy and France. Daniela Stojanova's co-authors include Sašo Džeroski, Andrej Kobler, Panče Panov, Michelangelo Ceci, Annalisa Appice, Donato Malerba, Bernard Ženko, Marko Bohanec, Aneta Trajanov and Marko Debeljak and has published in prestigious journals such as BMC Bioinformatics, Ecological Modelling and Data Mining and Knowledge Discovery.

In The Last Decade

Daniela Stojanova

9 papers receiving 310 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniela Stojanova Slovenia	7	125	108	81	76	58	10	322
Mingjie Qian China	10	88 0.7×	95 0.9×	96 1.2×	149 2.0×	25 0.4×	35	469
Guoyi Zhang United States	9	61 0.5×	33 0.3×	45 0.6×	41 0.5×	24 0.4×	31	360
Jorma Kilpi Finland	10	218 1.7×	229 2.1×	97 1.2×	62 0.8×	88 1.5×	25	545
Minhua Yang China	10	108 0.9×	164 1.5×	44 0.5×	41 0.5×	28 0.5×	20	419
Arnaud Le Bris France	12	169 1.4×	143 1.3×	67 0.8×	54 0.7×	40 0.7×	46	425
Hironori Hirata Japan	9	113 0.9×	37 0.3×	59 0.7×	101 1.3×	7 0.1×	78	341
Nan‐Jung Hsu Taiwan	12	119 1.0×	26 0.2×	45 0.6×	63 0.8×	15 0.3×	28	441
Wenhui Zhao China	12	63 0.5×	122 1.1×	198 2.4×	24 0.3×	11 0.2×	33	511
Zhu Jia China	6	45 0.4×	54 0.5×	33 0.4×	18 0.2×	58 1.0×	28	318

Countries citing papers authored by Daniela Stojanova

Since Specialization

Citations

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

Fields of papers citing papers by Daniela Stojanova

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniela Stojanova

This figure shows the co-authorship network connecting the top 25 collaborators of Daniela Stojanova. A scholar is included among the top collaborators of Daniela Stojanova 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 Daniela Stojanova. Daniela Stojanova 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:

10 of 10 papers shown

1.

Stojanova, Daniela, Michelangelo Ceci, Donato Malerba, & Sašo Džeroski. (2013). Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction. BMC Bioinformatics. 14(1). 285–285. 37 indexed citations

2.

Stojanova, Daniela. (2013). Considering Autocorrelation in Predictive Models. 37(1). 107–108. 2 indexed citations

3.

Debeljak, Marko, et al.. (2012). Using relational decision trees to model out-crossing rates in a multi-field setting. Ecological Modelling. 245. 75–83. 6 indexed citations

4.

Stojanova, Daniela, Michelangelo Ceci, Annalisa Appice, & Sašo Džeroski. (2012). Network regression with predictive clustering trees. Data Mining and Knowledge Discovery. 25(2). 378–413. 38 indexed citations

5.

Stojanova, Daniela, Michelangelo Ceci, Annalisa Appice, Donato Malerba, & Sašo Džeroski. (2012). Dealing with spatial autocorrelation when learning predictive clustering trees. Ecological Informatics. 13. 22–39. 35 indexed citations

6.

Stojanova, Daniela, et al.. (2011). Organization of fine root data obtained from minirhizotrons and ingrowth soil cores (how to construct an operational database using MS Access).. DiRROS repository (University of Maribor). 37–44. 1 indexed citations

7.

Stojanova, Daniela, et al.. (2011). Estimating the risk of fire outbreaks in the natural environment. Data Mining and Knowledge Discovery. 24(2). 411–442. 38 indexed citations

8.

Stojanova, Daniela, et al.. (2010). Estimating vegetation height and canopy cover from remotely sensed data with machine learning. Ecological Informatics. 5(4). 256–266. 145 indexed citations

9.

Stojanova, Daniela, et al.. (2007). A Qualitative Decision-Support Model for Evaluating Researchers. 31(4). 479–486. 7 indexed citations

10.

Stojanova, Daniela, et al.. (2006). Zeolites as alcohol adsorbents from aqueous solutions. Acta periodica technologica. 83–87. 13 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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