Bianca Dumitrascu

712 citations
17 papers · 301 indexed · h-index 9

Bianca Dumitrascu

16 papers receiving 297 citations

Peers

Bianca Dumitrascu
Comparison fields: 5 of 78
  • Biophysics 57
  • Molecular Biology 195
  • Cancer Research 36
  • Computational Mathematics 1
  • Artificial Intelligence 49
Replace Siyan Liu with:
Siyan Liu United States
Parisa Shooshtari Canada
Justina Žurauskienė United Kingdom
Mohamed Marouf Egypt
Raphael Petegrosso United States
Hussein Mohsen United States
Pierre Machart Germany
Matthew Ruffalo United States
Daniel Sumner Magruder Germany
Bianca Dumitrascu relative to Siyan Liu United States Siyan Liu's profile →
Citations per field
00.5×2.9×
Siyan Liu · 1×
Citations per year

Countries citing papers authored by Bianca Dumitrascu

Since Specialization
Citations

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

Fields of papers citing papers by Bianca Dumitrascu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Bianca Dumitrascu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bianca Dumitrascu Line = papers co-authored together Bianca Dumitrascu links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 20250
2 20256
3 20244
4 20243
5 202320
6 202328
7 20234
8 202122
9 202143
10 202136
11 20205
12 202062
13 202035
14
End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations.
20195
15
netNMF-sc: A Network Regularization Algorithm for Dimensionality Reduction and Imputation of Single-Cell Expression Data.
20191
16 20189
17 201818

About Bianca Dumitrascu

Bianca Dumitrascu is a scholar working on Biophysics, Molecular Biology and Artificial Intelligence, having authored 17 papers that have together received 301 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (8 papers), Gene expression and cancer classification (4 papers), Cell Image Analysis Techniques (4 papers), Gene Regulatory Network Analysis (3 papers), RNA Research and Splicing (2 papers), Economic and Environmental Valuation (1 paper), Orthopaedic implants and arthroplasty (1 paper) and Extracellular vesicles in disease (1 paper). The work is most often cited by research in Biophysics (57 citations), Molecular Biology (195 citations) and Cancer Research (36 citations). Bianca Dumitrascu has collaborated with scholars based in United States, United Kingdom and Denmark. Frequent co-authors include Barbara E. Engelhardt, Benjamin J. Raphael, Rebecca Elyanow, Soledad Villar, Adrien Hallou, Dustin G. Mixon, Virginie Uhlmann, Hannah Yevick, Aviv Regev and Sanja Vicković. Their work appears in journals such as Nature Communications, Bioinformatics and Development.

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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