Blaž Škrlj

810 total citations
40 papers, 393 citations indexed

About

Blaž Škrlj is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Blaž Škrlj has authored 40 papers receiving a total of 393 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 12 papers in Molecular Biology and 10 papers in Computational Theory and Mathematics. Recurrent topics in Blaž Škrlj's work include Bioinformatics and Genomic Networks (9 papers), Topic Modeling (8 papers) and Computational Drug Discovery Methods (8 papers). Blaž Škrlj is often cited by papers focused on Bioinformatics and Genomic Networks (9 papers), Topic Modeling (8 papers) and Computational Drug Discovery Methods (8 papers). Blaž Škrlj collaborates with scholars based in Slovenia, United Kingdom and Austria. Blaž Škrlj's co-authors include Senja Pollak, Nada Lavrač, Marko Robnik‐Šikonja, Matej Martinc, Janez Konc, Tanja Kunej, Dušanka Janežič, Samo Lešnik, Dragi Kocev and Meta Sterniša and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Blaž Škrlj

38 papers receiving 382 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Blaž Škrlj Slovenia 13 177 106 70 65 58 40 393
Axel J. Soto Canada 11 107 0.6× 71 0.7× 93 1.3× 44 0.7× 23 0.4× 37 298
Meena Nagarajan India 10 148 0.8× 86 0.8× 16 0.2× 56 0.9× 31 0.5× 24 501
Mohammed Alqarni Saudi Arabia 10 100 0.6× 134 1.3× 53 0.8× 42 0.6× 19 0.3× 22 435
Jiaqi Liu China 11 78 0.4× 43 0.4× 38 0.5× 35 0.5× 45 0.8× 47 335
Uzzal Kumar Acharjee Bangladesh 13 159 0.9× 45 0.4× 37 0.5× 105 1.6× 24 0.4× 41 488
Bertrand Mathieu France 12 158 0.9× 64 0.6× 12 0.2× 74 1.1× 36 0.6× 114 869
Yatao Bian China 11 182 1.0× 61 0.6× 80 1.1× 61 0.9× 44 0.8× 29 317
Dhanya Sridhar United States 6 244 1.4× 79 0.7× 77 1.1× 43 0.7× 42 0.7× 14 386
Yasuo Kudo Japan 11 124 0.7× 21 0.2× 94 1.3× 161 2.5× 20 0.3× 54 409
Alan Kuhnle United States 9 89 0.5× 78 0.7× 23 0.3× 33 0.5× 20 0.3× 33 279

Countries citing papers authored by Blaž Škrlj

Since Specialization
Citations

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

Fields of papers citing papers by Blaž Škrlj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Blaž Škrlj

This figure shows the co-authorship network connecting the top 25 collaborators of Blaž Škrlj. A scholar is included among the top collaborators of Blaž Škrlj 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 Blaž Škrlj. Blaž Škrlj 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.
Škrlj, Blaž. (2024). From Unimodal to Multimodal Machine Learning. SpringerBriefs in computer science.
2.
Škrlj, Blaž, et al.. (2023). To BAN or not to BAN. Repository of the University of Ljubljana (University of Ljubljana). 13 indexed citations
3.
Škrlj, Blaž, Borut Škodlar, Peter Pregelj, et al.. (2023). Dense attention network identifies EEG abnormalities during working memory performance of patients with schizophrenia. Frontiers in Psychiatry. 14. 1205119–1205119. 1 indexed citations
4.
Petković, Matej, Michelangelo Ceci, Gianvito Pio, et al.. (2022). Relational tree ensembles and feature rankings. Knowledge-Based Systems. 251. 109254–109254. 4 indexed citations
5.
Lavrač, Nada, et al.. (2022). Ontology Completion with Graph-Based Machine Learning: A Comprehensive Evaluation. SHILAP Revista de lepidopterología. 4(4). 1107–1123. 3 indexed citations
6.
Dermastia, Marina, Blaž Škrlj, Monika Riedle‐Bauer, et al.. (2021). Differential Response of Grapevine to Infection with ‘Candidatus Phytoplasma solani’ in Early and Late Growing Season through Complex Regulation of mRNA and Small RNA Transcriptomes. International Journal of Molecular Sciences. 22(7). 3531–3531. 11 indexed citations
7.
Škrlj, Blaž, Marus̆a Pompe‐Novak, Günter Brader, et al.. (2021). New Cross-Talks between Pathways Involved in Grapevine Infection with ‘Candidatus Phytoplasma solani’ Revealed by Temporal Network Modelling. Plants. 10(4). 646–646. 3 indexed citations
8.
Shekhar, Ravi, et al.. (2021). Zero-shot Cross-lingual Content Filtering: Offensive Language and Hate Speech Detection. Queen Mary Research Online (Queen Mary University of London). 30–34. 6 indexed citations
9.
Škrlj, Blaž, Matej Martinc, Nada Lavrač, & Senja Pollak. (2021). autoBOT: evolving neuro-symbolic representations for explainable low resource text classification. Machine Learning. 110(5). 989–1028. 18 indexed citations
10.
Škrlj, Blaž, et al.. (2021). A comprehensive comparison of molecular feature representations for use in predictive modeling. Computers in Biology and Medicine. 130. 104197–104197. 21 indexed citations
11.
Lavrač, Nada, et al.. (2021). Transfer Learning for Node Regression Applied to Spreading Prediction. Complex Systems. 30(4). 457–481. 1 indexed citations
12.
Škrlj, Blaž, et al.. (2021). PubMed-Scale Chemical Concept Embeddings Reconstruct Physical Protein Interaction Networks. SHILAP Revista de lepidopterología. 6. 644614–644614. 1 indexed citations
13.
Shekhar, Ravi, et al.. (2021). Investigating cross-lingual training for offensive language detection. PeerJ Computer Science. 7. e559–e559. 11 indexed citations
14.
Konc, Janez, Samo Lešnik, Blaž Škrlj, & Dušanka Janežič. (2021). ProBiS-Dock Database: A Web Server and Interactive Web Repository of Small Ligand–Protein Binding Sites for Drug Design. Journal of Chemical Information and Modeling. 61(8). 4097–4107. 18 indexed citations
15.
Škrlj, Blaž, et al.. (2020). CaNDis: a web server for investigation of causal relationships between diseases, drugs and drug targets. Bioinformatics. 37(6). 885–887. 5 indexed citations
16.
Škrlj, Blaž, et al.. (2019). CBSSD: community-based semantic subgroup discovery. Journal of Intelligent Information Systems. 53(2). 265–304. 6 indexed citations
17.
Martinc, Matej, Blaž Škrlj, & Senja Pollak. (2018). Multilingual Gender Classification with Multi-view Deep Learning: Notebook for PAN at CLEF 2018.. CLEF (Working Notes). 3 indexed citations
18.
Lešnik, Samo, et al.. (2017). BoBER: web interface to the base of bioisosterically exchangeable replacements. Journal of Cheminformatics. 9(1). 62–62. 12 indexed citations
19.
Konc, Janez, et al.. (2017). GenProBiS: web server for mapping of sequence variants to protein binding sites. Nucleic Acids Research. 45(W1). W253–W259. 12 indexed citations
20.
Škrlj, Blaž & Tanja Kunej. (2016). Computational identification of non-synonymous polymorphisms within regions corresponding to protein interaction sites. Computers in Biology and Medicine. 79. 30–35. 5 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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