Lan Žagar
Impact in
- Biophysics top 5%
- Spectroscopy Techniques in Biomedical and Chemical Research
- Analytical Chemistry top 5%
- Spectroscopy and Chemometric Analyses
Papers in
-
- Gene expression and cancer classification 4
- Single-cell and spatial transcriptomics 2
- Molecular Biology Techniques and Applications 2
- Pluripotent Stem Cells Research 2
- Genetics, Bioinformatics, and Biomedical Research 1
- Protein purification and stability 1
- Co-authors
- Blaž Zupan (7 shared papers)Tomaž Curk (2 shared papers)Janez Demšar (3 shared papers)Marko Toplak (2 shared papers)Miha Štajdohar (1 shared paper)Lan Umek (1 shared paper)Martin Možina (1 shared paper)Marinka Žitnik (1 shared paper)
- Journals
- Bioinformatics (2 papers)Genome biology (1 paper)Journal of Machine Learning Research (1 paper)Scientific Reports (1 paper)Methods of Information in Medicine (1 paper)
- Partner nations
- SloveniaItalyUnited States
In The Last Decade
Lan Žagar
8 papers receiving 1.5k citations
Lan Žagar's Hit Papers
Peers
Comparison fields: 5 of 203
- Biophysics 91
- Analytical Chemistry 74
- Aging 13
- Artificial Intelligence 220
- Computer Science Applications 34
Countries citing papers authored by Lan Žagar
This map shows the geographic impact of Lan Žagar'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 Lan Žagar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lan Žagar more than expected).
Fields of papers citing papers by Lan Žagar
This network shows the impact of papers produced by Lan Žagar. 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 Lan Žagar. The network helps show where Lan Žagar may publish in the future.
Co-authors
The 25 scholars most cited alongside Lan Žagar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Orange: data mining toolbox in python Hit paper breakdown → | 2013 | 1239 |
| 2 | 2010 | 139 | |
| 3 | 2019 | 61 | |
| 4 | 2021 | 56 | |
| 5 | 2011 | 15 | |
| 6 | 2019 | 7 | |
| 7 | 2012 | 5 | |
| 8 | 2012 | 1 |
About Lan Žagar
Lan Žagar is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition, Biophysics, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 8 papers that have together received 1.5k indexed citations. Recurring topics across this work include Gene expression and cancer classification (4 papers), Single-cell and spatial transcriptomics (2 papers), Cell Image Analysis Techniques (2 papers), Molecular Biology Techniques and Applications (2 papers), Pluripotent Stem Cells Research (2 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper), Computational Physics and Python Applications (1 paper) and Protein purification and stability (1 paper). The work is most often cited by research in Biophysics (91 citations), Analytical Chemistry (74 citations), Aging (13 citations), Artificial Intelligence (220 citations) and Computer Science Applications (34 citations). Lan Žagar has collaborated with scholars based in Slovenia, Italy and United States. Frequent co-authors include Blaž Zupan, Tomaž Curk, Janez Demšar, Marko Toplak, Miha Štajdohar, Lan Umek, Martin Možina, Marinka Žitnik, Tomaž Hočevar and Jure Žbontar. Their work appears in journals such as Bioinformatics, Genome biology, Journal of Machine Learning Research, Scientific Reports and Methods of Information in Medicine.
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.