Bosiljka Tasic

29.2k total citations · 4 hit papers
49 papers, 7.4k citations indexed

About

Bosiljka Tasic is a scholar working on Molecular Biology, Cognitive Neuroscience and Biophysics. According to data from OpenAlex, Bosiljka Tasic has authored 49 papers receiving a total of 7.4k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 13 papers in Cognitive Neuroscience and 12 papers in Biophysics. Recurrent topics in Bosiljka Tasic's work include Single-cell and spatial transcriptomics (19 papers), Neural dynamics and brain function (10 papers) and Cell Image Analysis Techniques (10 papers). Bosiljka Tasic is often cited by papers focused on Single-cell and spatial transcriptomics (19 papers), Neural dynamics and brain function (10 papers) and Cell Image Analysis Techniques (10 papers). Bosiljka Tasic collaborates with scholars based in United States, Germany and Sweden. Bosiljka Tasic's co-authors include Liqun Luo, Kazunari Miyamichi, Mandar D. Muzumdar, Ling Li, Tom Maniatis, Zizhen Yao, Lucas T. Graybuck, Hongkui Zeng, Christopher J. Potter and Liang Liang and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Bosiljka Tasic

46 papers receiving 7.4k citations

Hit Papers

A global double‐fluorescent Cre reporter mouse 2002 2026 2010 2018 2007 2018 2002 2023 500 1000 1.5k 2.0k 2.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bosiljka Tasic United States 28 4.7k 1.5k 931 781 664 49 7.4k
Seung Wook Oh United States 15 3.3k 0.7× 1.9k 1.3× 577 0.6× 1.0k 1.3× 462 0.7× 32 6.8k
Hirotaka James Okano Japan 44 3.8k 0.8× 1.5k 1.0× 534 0.6× 462 0.6× 519 0.8× 156 7.5k
Linda Madisen United States 27 4.1k 0.9× 2.5k 1.6× 905 1.0× 1.3k 1.6× 593 0.9× 40 8.7k
Frederick J. Livesey United Kingdom 42 6.2k 1.3× 2.0k 1.3× 907 1.0× 336 0.4× 795 1.2× 73 8.3k
Susan M. Dymecki United States 40 3.4k 0.7× 1.7k 1.1× 922 1.0× 700 0.9× 488 0.7× 69 6.2k
Hong Gu China 8 2.5k 0.5× 1.6k 1.1× 549 0.6× 816 1.0× 403 0.6× 16 5.3k
Lydia Ng United States 22 3.6k 0.8× 2.6k 1.7× 750 0.8× 1.8k 2.3× 507 0.8× 49 7.9k
Lin Gan United States 52 6.7k 1.4× 1.5k 1.0× 1.1k 1.2× 383 0.5× 1.0k 1.6× 187 9.6k
Jean‐François Brunet France 58 5.4k 1.2× 2.3k 1.5× 1.5k 1.6× 902 1.2× 836 1.3× 127 11.1k
Allan R. Jones United States 30 4.5k 1.0× 2.2k 1.4× 897 1.0× 1.3k 1.6× 529 0.8× 54 8.8k

Countries citing papers authored by Bosiljka Tasic

Since Specialization
Citations

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

Fields of papers citing papers by Bosiljka Tasic

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bosiljka Tasic

This figure shows the co-authorship network connecting the top 25 collaborators of Bosiljka Tasic. A scholar is included among the top collaborators of Bosiljka Tasic 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 Bosiljka Tasic. Bosiljka Tasic 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.
2.
Kunst, Michael, Shenqin Yao, Nicholas A. Lusk, et al.. (2025). Data-driven fine-grained region discovery in the mouse brain with transformers. Nature Communications. 16(1). 8536–8536.
3.
Zhang, Aixin, Lei Jin, Shenqin Yao, et al.. (2024). Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing. eLife. 12. 1 indexed citations
4.
Zhang, Aixin, Lei Jin, Shenqin Yao, et al.. (2023). Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing. eLife. 12. 6 indexed citations
5.
Bistrong, Karina, Shenqin Yao, Zizhen Yao, et al.. (2022). Dense functional and molecular readout of a circuit hub in sensory cortex. Science. 375(6576). eabl5981–eabl5981. 35 indexed citations
6.
Park, Jeongbin, Sebastian Tiesmeyer, Brian Long, et al.. (2021). Cell segmentation-free inference of cell types from in situ transcriptomics data. Nature Communications. 12(1). 3545–3545. 75 indexed citations
7.
Park, Jeongbin, Sebastian Tiesmeyer, Brian Long, et al.. (2021). Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data. Nature Communications. 12(1). 4103–4103. 1 indexed citations
8.
Gala, Rohan, Agata Budzillo, Fahimeh Baftizadeh, et al.. (2021). Consistent cross-modal identification of cortical neurons with coupled autoencoders. Nature Computational Science. 1(2). 120–127. 23 indexed citations
9.
Miller, Jeremy A., Nathan W. Gouwens, Bosiljka Tasic, et al.. (2020). Common cell type nomenclature for the mammalian brain. eLife. 9. 38 indexed citations
10.
Smith, Stephen J, Uygar Sümbül, Lucas T. Graybuck, et al.. (2019). Single-cell transcriptomic evidence for dense intracortical neuropeptide networks. eLife. 8. 90 indexed citations
11.
Rosenberg, Alexander, Charles M. Roco, Richard A. Muscat, et al.. (2018). Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 360(6385). 176–182. 877 indexed citations breakdown →
12.
Zhu, Fangfang, Simon Hippenmeyer, Bosiljka Tasic, et al.. (2013). DICE, an efficient system for iterative genomic editing in human pluripotent stem cells. Nucleic Acids Research. 42(5). e34–e34. 87 indexed citations
13.
Chen, Weisheng V., Francisco J. Álvarez, Julie L. Lefebvre, et al.. (2012). Functional Significance of Isoform Diversification in the Protocadherin Gamma Gene Cluster. Neuron. 75(5). 928–929. 2 indexed citations
14.
Tasic, Bosiljka, Kazunari Miyamichi, Simon Hippenmeyer, et al.. (2012). Extensions of MADM (Mosaic Analysis with Double Markers) in Mice. PLoS ONE. 7(3). e33332–e33332. 49 indexed citations
15.
Tasic, Bosiljka, et al.. (2011). Site-specific integrase-mediated transgenesis in mice via pronuclear injection. Proceedings of the National Academy of Sciences. 108(19). 7902–7907. 180 indexed citations
16.
Potter, Christopher J., et al.. (2010). The Q System: A Repressible Binary System for Transgene Expression, Lineage Tracing, and Mosaic Analysis. Cell. 141(3). 536–548. 406 indexed citations
17.
Li, Ling, Bosiljka Tasic, Kristina D. Micheva, et al.. (2010). Visualizing the Distribution of Synapses from Individual Neurons in the Mouse Brain. PLoS ONE. 5(7). e11503–e11503. 87 indexed citations
18.
Muzumdar, Mandar D., Bosiljka Tasic, Kazunari Miyamichi, Ling Li, & Liqun Luo. (2007). A global double‐fluorescent Cre reporter mouse. genesis. 45(9). 593–605. 2568 indexed citations breakdown →
19.
Maniatis, Tom & Bosiljka Tasic. (2002). Alternative pre-mRNA splicing and proteome expansion in metazoans. Nature. 418(6894). 236–243. 601 indexed citations breakdown →
20.
Tasic, Bosiljka, Christoph E. Nabholz, Kristin K. Baldwin, et al.. (2002). Promoter Choice Determines Splice Site Selection in Protocadherin α and γ Pre-mRNA Splicing. Molecular Cell. 10(1). 21–33. 244 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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