Miha Škalič
- Molecular Biology top 10%
- Computational Theory and Mathematics top 0.5%
- Materials Chemistry top 10%
- Cancer Research
- Pharmacology top 10%
- Co-authors
- José Jiménez-LunaGianni De FabritiisGerard Martínez-RosellDavide SabbadinEduardo EyrasDavid J. ElliottJuan L. TrincadoJuan Carlos Entizne
- Topics
- Computational Drug Discovery Methods (9 papers)Machine Learning in Materials Science (7 papers)Protein Structure and Dynamics (4 papers)
- Partner nations
- SpainGermanySwitzerland
In The Last Decade
Miha Škalič
11 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Molecular Biology 1.1k
- Computational Theory and Mathematics 844
- Materials Chemistry 445
- Cancer Research 104
- Pharmacology 98
Countries citing papers authored by Miha Škalič
This map shows the geographic impact of Miha Škalič'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 Miha Škalič with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miha Škalič more than expected).
Fields of papers citing papers by Miha Škalič
This network shows the impact of papers produced by Miha Škalič. 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 Miha Škalič. The network helps show where Miha Škalič may publish in the future.
Co-authorship network of co-authors of Miha Škalič
This figure shows the co-authorship network connecting the top 25 collaborators of Miha Škalič. A scholar is included among the top collaborators of Miha Škalič 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 Miha Škalič. Miha Škalič is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 4 | |
| 3 | 18 | |
| 4 | 57 | |
| 5 | 14 | |
| 6 | 92 | |
| 7 | 150 | |
| 8 | SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditionsbreakdown → | 355 |
| 9 | 46 | |
| 10 | KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networksbreakdown → | 637 |
| 11 | 42 |
About Miha Škalič
Miha Škalič is a scholar working on Computational Theory and Mathematics, Biophysics and Materials Chemistry, having authored 11 papers that have together received 1.4k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Machine Learning in Materials Science (7 papers) and Protein Structure and Dynamics (4 papers). The work is most often cited by research in Computational Theory and Mathematics (844 citations), Molecular Biology (1.1k citations) and Materials Chemistry (445 citations). Miha Škalič has collaborated with scholars based in Spain, Germany and Switzerland. Frequent co-authors include José Jiménez-Luna, Gianni De Fabritiis, Gerard Martínez-Rosell, Davide Sabbadin, Eduardo Eyras, David J. Elliott, Juan L. Trincado, Juan Carlos Entizne, Gerald Hysenaj and Babita Singh. Their work appears in journals such as Bioinformatics, Genome biology and Journal of Chemical Information and Modeling.
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.