Maria Brbić

3.3k total citations · 2 hit papers
18 papers, 950 citations indexed

About

Maria Brbić is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Biophysics. According to data from OpenAlex, Maria Brbić has authored 18 papers receiving a total of 950 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Biophysics. Recurrent topics in Maria Brbić's work include Single-cell and spatial transcriptomics (6 papers), Cell Image Analysis Techniques (5 papers) and Face and Expression Recognition (4 papers). Maria Brbić is often cited by papers focused on Single-cell and spatial transcriptomics (6 papers), Cell Image Analysis Techniques (5 papers) and Face and Expression Recognition (4 papers). Maria Brbić collaborates with scholars based in United States, Croatia and United Kingdom. Maria Brbić's co-authors include Ivica Kopriva, Jure Leskovec, Anita Kriško, Fran Supek, Russ B. Altman, Sheng Wang, Spyros Darmanis, Marinka Žitnik, Angela Oliveira Pisco and Vedrana Vidulin and has published in prestigious journals such as Nature, Nucleic Acids Research and Nature Communications.

In The Last Decade

Maria Brbić

18 papers receiving 944 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Multi-view low-rank sparse subspace clustering

2017 344 citations Maria Brbić, Ivica Kopriva Pattern Recognition profile →
Towards multimodal foundation models in molecular cell biology

2025 22 citations Haotian Cui, Maria Brbić et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maria Brbić United States	13	418	271	229	161	135	18	950
Yingnian Wu China	9	421 1.0×	738 2.7×	91 0.4×	59 0.4×	86 0.6×	37	1.5k
Eva L. Dyer United States	10	93 0.2×	50 0.2×	78 0.3×	74 0.5×	30 0.2×	37	547
Paolo Emilio Barbano United States	9	104 0.2×	418 1.5×	74 0.3×	30 0.2×	26 0.2×	16	720
Brian Wylie United States	12	257 0.6×	483 1.8×	107 0.5×	14 0.1×	11 0.1×	21	1.0k
Mario Ferraro Italy	14	254 0.6×	100 0.4×	44 0.2×	40 0.2×	35 0.3×	63	639
Kenneth R. Castleman United States	20	411 1.0×	491 1.8×	172 0.8×	16 0.1×	93 0.7×	71	1.1k
Tanel Pärnamaa Estonia	5	78 0.2×	599 2.2×	228 1.0×	9 0.1×	51 0.4×	8	1.2k
Soheil Feizi United States	16	184 0.4×	595 2.2×	284 1.2×	45 0.3×	9 0.1×	69	1.3k
Rui Kuang United States	24	90 0.2×	987 3.6×	249 1.1×	36 0.2×	6 0.0×	74	1.6k
Manuel G. Forero Colombia	17	175 0.4×	353 1.3×	46 0.2×	214 1.3×	220 1.6×	68	1.1k

Countries citing papers authored by Maria Brbić

Since Specialization

Citations

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

Fields of papers citing papers by Maria Brbić

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maria Brbić

This figure shows the co-authorship network connecting the top 25 collaborators of Maria Brbić. A scholar is included among the top collaborators of Maria Brbić 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 Maria Brbić. Maria Brbić is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Cui, Haotian, Maria Brbić, Julio Sáez-Rodríguez, et al.. (2025). Towards multimodal foundation models in molecular cell biology. Nature. 640(8059). 623–633. 22 indexed citations breakdown →

Viñas, Ramón, Zoe Piran, Shilong Fan, et al.. (2025). Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. Nature Biotechnology. 3 indexed citations

Rosen, Yanay, et al.. (2024). Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN. Nature Methods. 21(8). 1492–1500. 32 indexed citations

Brbić, Maria, Kaidi Cao, John W. Hickey, et al.. (2022). Annotation of spatially resolved single-cell data with STELLAR. Nature Methods. 19(11). 1411–1418. 44 indexed citations

Wang, Sheng, Angela Oliveira Pisco, Aaron McGeever, et al.. (2021). Leveraging the Cell Ontology to classify unseen cell types. Nature Communications. 12(1). 5556–5556. 26 indexed citations

McLaughlin, Colleen N., Maria Brbić, Qijing Xie, et al.. (2021). Single-cell transcriptomes of developing and adult olfactory receptor neurons in Drosophila. eLife. 10. 68 indexed citations

Xie, Qijing, Maria Brbić, Felix Horns, et al.. (2021). Temporal evolution of single-cell transcriptomes of Drosophila olfactory projection neurons. eLife. 10. 34 indexed citations

Task, Darya, Chun‐Chieh Lin, Ali Afify, et al.. (2021). Chemoreceptor co-expression in Drosophila melanogaster olfactory neurons.. Apollo (University of Cambridge). 81 indexed citations

Brbić, Maria, Marinka Žitnik, Sheng Wang, et al.. (2020). MARS: discovering novel cell types across heterogeneous single-cell experiments. Nature Methods. 17(12). 1200–1206. 89 indexed citations

10.

Cao, Kaidi, Maria Brbić, & Jure Leskovec. (2020). Concept Learners for Few-Shot Learning. arXiv (Cornell University). 1 indexed citations

11.

Brbić, Maria, et al.. (2019). Selective Sampling-based Scalable Sparse Subspace Clustering. Neural Information Processing Systems. 32. 12416–12425. 28 indexed citations

12.

Brbić, Maria & Ivica Kopriva. (2018). $\ell_0$ -Motivated Low-Rank Sparse Subspace Clustering. IEEE Transactions on Cybernetics. 50(4). 1711–1725. 72 indexed citations

13.

Brbić, Maria & Ivica Kopriva. (2018). 𝓁 0 -Motivated Low-Rank Sparse Subspace Clustering.. 3 indexed citations

14.

Brbić, Maria & Ivica Kopriva. (2017). Multi-view low-rank sparse subspace clustering. Pattern Recognition. 73. 247–258. 344 indexed citations breakdown →

15.

Brbić, Maria, et al.. (2016). The landscape of microbial phenotypic traits and associated genes. Nucleic Acids Research. 44(21). gkw964–gkw964. 81 indexed citations

16.

Brbić, Maria, Tobias Warnecke, Anita Kriško, & Fran Supek. (2015). Global Shifts in Genome and Proteome Composition Are Very Tightly Coupled. Genome Biology and Evolution. 7(6). 1519–1532. 18 indexed citations

17.

Brbić, Maria & Ivana Podnar Žarko. (2015). Tuning machine learning algorithms for content-based movie recommendation. Intelligent Decision Technologies. 9(3). 233–242. 3 indexed citations

18.

Brbić, Maria, et al.. (2012). Recommendation of YouTube Videos. International Convention on Information and Communication Technology, Electronics and Microelectronics. 1775–1779. 1 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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