Ali Foroughi pour

466 total citations
29 papers, 255 citations indexed

About

Ali Foroughi pour is a scholar working on Molecular Biology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ali Foroughi pour has authored 29 papers receiving a total of 255 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 12 papers in Artificial Intelligence and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ali Foroughi pour's work include Gene expression and cancer classification (17 papers), Machine Learning in Bioinformatics (9 papers) and Bioinformatics and Genomic Networks (9 papers). Ali Foroughi pour is often cited by papers focused on Gene expression and cancer classification (17 papers), Machine Learning in Bioinformatics (9 papers) and Bioinformatics and Genomic Networks (9 papers). Ali Foroughi pour collaborates with scholars based in United States. Ali Foroughi pour's co-authors include Lori A. Dalton, Jeffrey H. Chuang, Sandeep Namburi, Saman Farahmand, David L. Rimm, Kourosh Zarringhalam, Dennis L. Caruana, Javad Noorbakhsh, Todd Sheridan and Brian S. White and has published in prestigious journals such as Nature Communications, Cancer Research and Scientific Reports.

In The Last Decade

Ali Foroughi pour

28 papers receiving 253 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ali Foroughi pour United States 8 140 96 90 44 34 29 255
Saman Farahmand United States 6 162 1.2× 133 1.4× 88 1.0× 65 1.5× 32 0.9× 11 308
Apaar Sadhwani United States 4 151 1.1× 139 1.4× 25 0.3× 39 0.9× 22 0.6× 6 248
Paul van Diest Netherlands 4 186 1.3× 127 1.3× 101 1.1× 55 1.3× 48 1.4× 4 349
Andrew Lagree Canada 11 151 1.1× 192 2.0× 29 0.3× 52 1.2× 15 0.4× 15 295
Chiara Maria Lavinia Loeffler Germany 8 108 0.8× 112 1.2× 43 0.5× 73 1.7× 24 0.7× 12 240
Sahirzeeshan Ali United States 10 259 1.9× 183 1.9× 73 0.8× 47 1.1× 57 1.7× 15 362
Karsten Wendt Germany 8 96 0.7× 65 0.7× 40 0.4× 18 0.4× 20 0.6× 16 267
Can Koyuncu United States 10 123 0.9× 106 1.1× 30 0.3× 62 1.4× 73 2.1× 21 253
Trissia Brown United States 5 106 0.8× 115 1.2× 46 0.5× 45 1.0× 32 0.9× 5 220
Venkata N. P. Vemuri United States 4 143 1.0× 98 1.0× 91 1.0× 51 1.2× 75 2.2× 6 305

Countries citing papers authored by Ali Foroughi pour

Since Specialization
Citations

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

Fields of papers citing papers by Ali Foroughi pour

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ali Foroughi pour. 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 Ali Foroughi pour. The network helps show where Ali Foroughi pour may publish in the future.

Co-authorship network of co-authors of Ali Foroughi pour

This figure shows the co-authorship network connecting the top 25 collaborators of Ali Foroughi pour. A scholar is included among the top collaborators of Ali Foroughi pour 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 Ali Foroughi pour. Ali Foroughi pour 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.
Noorbakhsh, Javad, Ali Foroughi pour, & Jeffrey H. Chuang. (2025). Emerging AI approaches for cancer spatial omics. GigaScience. 14. 1 indexed citations
2.
pour, Ali Foroughi, Santhosh Sivajothi, Bonnie Choy, et al.. (2024). Abstract PR011: Imaging mass cytometry captures patient heterogeneity enabling BCG response stratification in non-muscle invasive bladder cancer. Clinical Cancer Research. 30(10_Supplement). PR011–PR011.
3.
Martínek, Jan, et al.. (2024). Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments. Communications Biology. 7(1). 1201–1201. 3 indexed citations
4.
Zhou, Jie, et al.. (2023). Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality. EBioMedicine. 94. 104726–104726. 16 indexed citations
5.
Sheridan, Todd, et al.. (2023). SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images. EBioMedicine. 99. 104908–104908. 9 indexed citations
6.
Martínek, Jan, et al.. (2023). Abstract 5883: Computational analysis of immune synapses in melanoma tumor microenvironment. Cancer Research. 83(7_Supplement). 5883–5883. 1 indexed citations
7.
pour, Ali Foroughi, Brian S. White, Jonghanne Park, Todd Sheridan, & Jeffrey H. Chuang. (2022). Deep learning features encode interpretable morphologies within histological images. Scientific Reports. 12(1). 9428–9428. 17 indexed citations
8.
Rubinstein, Jill C., Ali Foroughi pour, Jie Zhou, et al.. (2022). Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer. Journal of Surgical Oncology. 127(3). 426–433. 7 indexed citations
9.
Noorbakhsh, Javad, Saman Farahmand, Ali Foroughi pour, et al.. (2021). Abstract PO-003: Deep learning identifies conserved pan-cancer tumor features. Clinical Cancer Research. 27(5_Supplement). PO–3. 1 indexed citations
10.
pour, Ali Foroughi & Lori A. Dalton. (2020). Theory of Optimal Bayesian Feature Filtering. Project Euclid (Cornell University). 1 indexed citations
11.
Noorbakhsh, Javad, Saman Farahmand, Ali Foroughi pour, et al.. (2020). Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nature Communications. 11(1). 6367–6367. 124 indexed citations
12.
pour, Ali Foroughi, et al.. (2020). The effect of blurring on lung cancer subtype classification accuracy of convolutional neural networks. 2987–2989. 3 indexed citations
13.
pour, Ali Foroughi, Maciej Pietrzak, Lara E. Sucheston‐Campbell, et al.. (2020). High dimensional model representation of log likelihood ratio: binary classification with SNP data. BMC Medical Genomics. 13(S9). 133–133. 1 indexed citations
14.
pour, Ali Foroughi, Maciej Pietrzak, Lori A. Dalton, & Grzegorz A. Rempała. (2020). High dimensional model representation of log-likelihood ratio: binary classification with expression data. BMC Bioinformatics. 21(1). 156–156. 1 indexed citations
15.
pour, Ali Foroughi & Lori A. Dalton. (2018). Heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure. BMC Bioinformatics. 19(S3). 70–70. 6 indexed citations
16.
pour, Ali Foroughi & Lori A. Dalton. (2017). Multiclass Bayesian feature selection. 31. 725–729. 2 indexed citations
17.
pour, Ali Foroughi & Lori A. Dalton. (2017). Robust feature selection for block covariance Bayesian models. 31. 2696–2700. 9 indexed citations
18.
pour, Ali Foroughi & Lori A. Dalton. (2016). Multiple Sclerosis Biomarker Discovery via Bayesian Feature Selection. 540–541. 6 indexed citations
19.
pour, Ali Foroughi & Lori A. Dalton. (2016). Optimal Bayesian feature selection with missing data. 35–39. 3 indexed citations
20.
pour, Ali Foroughi & Lori A. Dalton. (2015). Optimal bayesian feature filtering. 57. 651–652. 10 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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