Bahram Parvin

11.0k total citations
84 papers, 1.8k citations indexed

About

Bahram Parvin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Bahram Parvin has authored 84 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Computer Vision and Pattern Recognition, 22 papers in Artificial Intelligence and 18 papers in Molecular Biology. Recurrent topics in Bahram Parvin's work include AI in cancer detection (21 papers), Cell Image Analysis Techniques (16 papers) and Medical Image Segmentation Techniques (14 papers). Bahram Parvin is often cited by papers focused on AI in cancer detection (21 papers), Cell Image Analysis Techniques (16 papers) and Medical Image Segmentation Techniques (14 papers). Bahram Parvin collaborates with scholars based in United States, China and Norway. Bahram Parvin's co-authors include Hang Chang, Paul T. Spellman, Mary Helen Barcellos‐Hoff, Alexander D. Borowsky, Sylvain V. Costes, Yin Zhou, Shraddha A. Ravani, Kenneth E. Barner, Gerald Fontenay and Rosa Anna DeFilippis and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Bioinformatics and PLoS ONE.

In The Last Decade

Bahram Parvin

81 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bahram Parvin United States	24	586	533	500	359	346	84	1.8k
Constantino Carlos Reyes‐Aldasoro United Kingdom	24	514 0.9×	670 1.3×	377 0.8×	405 1.1×	632 1.8×	99	2.6k
Ewert Bengtsson Sweden	29	787 1.3×	867 1.6×	853 1.7×	347 1.0×	588 1.7×	135	3.0k
António Polónia Portugal	18	397 0.7×	977 1.8×	361 0.7×	366 1.0×	809 2.3×	49	1.7k
Nicolas Coudray United States	17	554 0.9×	1.2k 2.2×	258 0.5×	495 1.4×	1.1k 3.1×	49	2.5k
Yi Gao China	21	301 0.5×	845 1.6×	626 1.3×	274 0.8×	764 2.2×	109	2.1k
Cleo‐Aron Weis Germany	22	375 0.6×	844 1.6×	315 0.6×	703 2.0×	713 2.1×	71	2.3k
Cheng Lu China	33	744 1.3×	980 1.8×	1.2k 2.4×	764 2.1×	817 2.4×	197	3.8k
David J. Foran United States	30	517 0.9×	1.2k 2.2×	1.0k 2.0×	312 0.9×	795 2.3×	110	3.1k
Natalie Shih United States	24	718 1.2×	1.4k 2.7×	750 1.5×	494 1.4×	1.1k 3.3×	47	2.8k

Countries citing papers authored by Bahram Parvin

Since Specialization

Citations

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

Fields of papers citing papers by Bahram Parvin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bahram Parvin

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

All Works

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20 of 20 papers shown

Parvin, Bahram, et al.. (2025). Multi-Aperture Fusion of Transformer-Convolutional Networks With Curvature-Aware Loss Function Enhances 3D Segmentation of Clinical and Microscopy Images. IEEE Journal of Biomedical and Health Informatics. 29(12). 8591–8598.

Lee, Jingyun, et al.. (2025). Plasma Proteomic Analysis Reveals Complement System Changes in Irradiated Female BALB/c Mice during Mammary Carcinogenesis. Cancer Research Communications. 5(8). 1409–1418.

Chien, Lung-Chang, et al.. (2023). Influence of Simulated Microgravity on Mammary Epithelial Cells Grown as 2D and 3D Cultures. International Journal of Molecular Sciences. 24(8). 7615–7615. 2 indexed citations

Yaswen, Paul, et al.. (2021). Protein Ligands in the Secretome of CD36+ Fibroblasts Induce Growth Suppression in a Subset of Breast Cancer Cell Lines. Cancers. 13(18). 4521–4521. 11 indexed citations

Cheng, Qingsu, et al.. (2020). Overexpression of CD36 in mammary fibroblasts suppresses colony growth in breast cancer cell lines. Biochemical and Biophysical Research Communications. 526(1). 41–47. 10 indexed citations

Cheng, Qingsu & Bahram Parvin. (2020). Rapid identification of a subset of foodborne bacteria in live-cell assays. Applied Microbiology and Biotechnology. 104(24). 10571–10584. 1 indexed citations

Vatter, Fanny A. Pelissier, Denis Schapiro, Hang Chang, et al.. (2018). High-Dimensional Phenotyping Identifies Age-Emergent Cells in Human Mammary Epithelia. Cell Reports. 23(4). 1205–1219. 32 indexed citations

Hamidi, Reza Jalilzadeh, et al.. (2017). Electromagnetic transient events (EMTE) classification in transmission grids. 1–5. 5 indexed citations

Cheng, Qingsu & Bahram Parvin. (2017). Buckyballs conjugated with nucleic acid sequences identifies microorganisms in live cell assays. Journal of Nanobiotechnology. 15(1). 78–78. 2 indexed citations

10.

Cheng, Qingsu, Gerald Fontenay, Hang Chang, et al.. (2016). Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density. Scientific Reports. 6(1). 28987–28987. 17 indexed citations

11.

Zhou, Yin, Hang Chang, Kenneth E. Barner, & Bahram Parvin. (2015). Nuclei segmentation via sparsity constrained convolutional regression. PubMed. 2015. 1284–1287. 18 indexed citations

12.

Chang, Hang, Yin Zhou, Paul T. Spellman, & Bahram Parvin. (2013). Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology. PubMed. 169–176. 21 indexed citations

13.

DeFilippis, Rosa Anna, Hang Chang, Nancy Dumont, et al.. (2012). CD36 Repression Activates a Multicellular Stromal Program Shared by High Mammographic Density and Tumor Tissues. Cancer Discovery. 2(9). 826–839. 152 indexed citations

14.

Chang, Hang, Gerald Fontenay, James Chen, et al.. (2011). Persistence of γ-H2AX and 53BP1 foci in proliferating and non-proliferating human mammary epithelial cells after exposure to γ-rays or iron ions. International Journal of Radiation Biology. 87(7). 696–710. 32 indexed citations

15.

Zhang, Kai, Joe W. Gray, & Bahram Parvin. (2010). Sparse multitask regression for identifying common mechanism of response to therapeutic targets. Bioinformatics. 26(12). i97–i105. 27 indexed citations

16.

Sadanandam, Anguraj, Steffen Durinck, Shivani Nautiyal, et al.. (2010). Prediction of epigenetically regulated genes in breast cancer cell lines. BMC Bioinformatics. 11(1). 305–305. 31 indexed citations

17.

Han, Junwei, et al.. (2010). Multiscale iterative voting for differential analysis of stress response for 2D and 3D cell culture models. Journal of Microscopy. 241(3). 315–326. 16 indexed citations

18.

Silva, Claudio, George Bebis, Renato Pajarola, et al.. (2009). Advances in Visual Computing : 5th International Symposium, ISVC 2009, Las Vegas, NV, USA, November 30-December 2, 2009. Proceedings, Part II. Springer eBooks. 1 indexed citations

19.

Maxwell, Christopher A., Markus C. Fleisch, Sylvain V. Costes, et al.. (2008). Targeted and Nontargeted Effects of Ionizing Radiation That Impact Genomic Instability. Cancer Research. 68(20). 8304–8311. 75 indexed citations

20.

Bebis, George, Richard Boyle, Bahram Parvin, et al.. (2008). Proceedings of the 4th International Symposium on Advances in Visual Computing. 4 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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