Ali Foroughi pour
- Biophysics top 10%
- Artificial Intelligence top 10%
- AI in cancer detection 7
- Bayesian Methods and Mixture Models 3
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- Radiomics and Machine Learning in Medical Imaging 7
- Statistics and Probability top 10%
- Statistical Methods and Inference 5
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- Gene expression and cancer classification 17
- Machine Learning in Bioinformatics 9
- Bioinformatics and Genomic Networks 9
- Single-cell and spatial transcriptomics 3
- Co-authors
- Lori A. DaltonJeffrey H. ChuangDavid L. RimmKourosh ZarringhalamJavad NoorbakhshDennis L. CaruanaSandeep NamburiSaman Farahmand
- Partner nations
- United States
In The Last Decade
Ali Foroughi pour
28 papers receiving 253 citations
Peers
Comparison fields: 5 of 58
- Biophysics 34
- Artificial Intelligence 140
- Radiology, Nuclear Medicine and Imaging 96
- Statistics and Probability 23
- Cancer Research 34
Countries citing papers authored by Ali Foroughi pour
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
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
The 25 scholars most cited alongside Ali Foroughi pour, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 3 | |
| 4 | 2023 | 9 | |
| 5 | 2023 | 16 | |
| 6 | 2023 | 1 | |
| 7 | 2022 | 17 | |
| 8 | 2022 | 7 | |
| 9 | 2021 | 1 | |
| 10 | 2020 | 1 | |
| 11 | 2020 | 124 | |
| 12 | 2020 | 1 | |
| 13 | 2020 | 3 | |
| 14 | 2020 | 1 | |
| 15 | 2019 | 2 | |
| 16 | 2018 | 6 | |
| 17 | 2018 | 5 | |
| 18 | 2017 | 2 | |
| 19 | 2017 | 9 | |
| 20 | 2015 | 10 |
About Ali Foroughi pour
Ali Foroughi pour is a scholar working on Statistics and Probability, Biophysics and Artificial Intelligence, having authored 29 papers that have together received 255 indexed citations. Recurring topics across this work include Gene expression and cancer classification (17 papers), Machine Learning in Bioinformatics (9 papers), Bioinformatics and Genomic Networks (9 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (7 papers), Statistical Methods and Inference (5 papers), Bayesian Methods and Mixture Models (3 papers) and Single-cell and spatial transcriptomics (3 papers). The work is most often cited by research in Biophysics (34 citations), Artificial Intelligence (140 citations) and Radiology, Nuclear Medicine and Imaging (96 citations). Ali Foroughi pour has collaborated with scholars based in United States. Frequent co-authors include Lori A. Dalton, Jeffrey H. Chuang, David L. Rimm, Kourosh Zarringhalam, Javad Noorbakhsh, Dennis L. Caruana, Sandeep Namburi, Saman Farahmand, Todd Sheridan and Brian S. White. Their work appears in journals such as Clinical Cancer Research, BMC Bioinformatics, EBioMedicine, Cancer Research and BMC Medical Genomics.
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.