Hossein Farahani

2.3k total citations
31 papers, 596 citations indexed

About

Hossein Farahani is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hossein Farahani has authored 31 papers receiving a total of 596 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 6 papers in Molecular Biology and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hossein Farahani's work include AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Composite Structure Analysis and Optimization (4 papers). Hossein Farahani is often cited by papers focused on AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Composite Structure Analysis and Optimization (4 papers). Hossein Farahani collaborates with scholars based in Canada, Iran and United States. Hossein Farahani's co-authors include Jens Lagergren, Ali Bashashati, Mikaela Behm, Marie Öhman, C. Blake Gilks, David G. Huntsman, Sohrab P. Shah, Stephen Yip, Steven J.M. Jones and David Farnell and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Hossein Farahani

29 papers receiving 581 citations

Peers

Hossein Farahani

RNMI

Tinghui Wu Taiwan

Xiguo Yuan China

Margaret Guo United States

Ge Lou China

Trond Hellem Bø Norway

Rong Ma United States

Sanghoon Lee United States

Deborah Thompson United States

Ken Takasawa Japan

Xiangxue Wang United States

Tinghui Wu Taiwan

Hossein Farahani

106 ×0.4

148 ×0.9

109 ×0.8

99 ×0.7

179 ×1.7

RNMI

Citations per year, relative to Hossein Farahani Hossein Farahani (= 1×) peers Tinghui Wu

Countries citing papers authored by Hossein Farahani

Since Specialization

Citations

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

Fields of papers citing papers by Hossein Farahani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hossein Farahani

This figure shows the co-authorship network connecting the top 25 collaborators of Hossein Farahani. A scholar is included among the top collaborators of Hossein Farahani 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 Hossein Farahani. Hossein Farahani 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

Farnell, David, et al.. (2025). Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images. Discover Oncology. 16(1). 196–196. 1 indexed citations

Liu, Haoran, et al.. (2025). KAFSTExp: Kernel Adaptive Filtering With Nyström Approximation for Predicting Spatial Gene Expression From Histology Images. IEEE Journal of Biomedical and Health Informatics. 30(3). 2203–2216. 1 indexed citations

Chu, Jenny E., David Farnell, David J. Schaeffer, et al.. (2025). A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images. Cancers. 17(18). 2991–2991.

Farahani, Hossein, Maryam Asadi, Matthew O. Wiens, et al.. (2024). AI-based histopathology image analysis reveals a distinct subset of endometrial cancers. Nature Communications. 15(1). 4973–4973. 13 indexed citations

Zhang, Allen, Katy Milne, Steven J.M. Jones, et al.. (2024). VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology. Nature Communications. 15(1). 3942–3942. 4 indexed citations

Zhang, Allen, Alberto Contreras‐Sanz, Martin Köbel, et al.. (2024). Learning generalizable AI models for multi-center histopathology image classification. npj Precision Oncology. 8(1). 151–151. 12 indexed citations

Farahani, Hossein, et al.. (2024). Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries. Genome Research. 34(8). 1196–1210. 1 indexed citations

Farahani, Hossein, David Farnell, James T. Topham, et al.. (2024). A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images. American Journal Of Pathology. 194(12). 2302–2312. 3 indexed citations

Farahani, Hossein, Mostafa Najafi, Mohammad Behbahani, & Mohammad Taghi Naseri. (2023). Ultrasonic assisted magnetic dispersive solid phase extraction of 2-chloroethyl ethyl sulfide by magnetic activated carbon from aqueous samples prior to gas chromatography-ion mobility spectrometry analysis. Microchemical Journal. 193. 109146–109146. 7 indexed citations

10.

Farahani, Hossein, David Farnell, Allen Zhang, et al.. (2022). Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images. Modern Pathology. 35(12). 1983–1990. 42 indexed citations

11.

Naso, Julia, Adrian Levine, Hossein Farahani, et al.. (2021). Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations. Modern Pathology. 34(11). 2028–2035. 15 indexed citations

12.

Farahani, Hossein, et al.. (2020). Physiological and biochemical responses of Damask rose (Rosa damascena Miller) to potassium silicate application under water deficit stress. Notulae Botanicae Horti Agrobotanici Cluj-Napoca. 48(3). 1560–1572. 5 indexed citations

13.

Campbell, Kieran R., Adi Steif, Emma Laks, et al.. (2019). clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers. Genome biology. 20(1). 54–54. 69 indexed citations

14.

Farahani, Hossein, Camila P. E. de Souza, Damian Yap, et al.. (2017). Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer. Scientific Reports. 7(1). 13467–13467. 3 indexed citations

15.

Anglesio, Michael S., Ali Bashashati, Yi Kan Wang, et al.. (2015). Multifocal endometriotic lesions associated with cancer are clonal and carry a high mutation burden. The Journal of Pathology. 236(2). 201–209. 116 indexed citations

16.

Farahani, Hossein, et al.. (2015). Vibration Analysis of Composite Horizontal Cylindrical Tank with Different Layering Using the Finite Element Method. Indian Journal of Science and Technology. 8(S7). 213–213. 6 indexed citations

17.

Parviainen, Pekka, Hossein Farahani, & Jens Lagergren. (2014). Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming. Journal of Machine Learning Research. 33. 751–759. 20 indexed citations

18.

Farahani, Hossein & Jens Lagergren. (2013). Learning oncogenetic networks by reducing to MILP. PLoS ONE. 3 indexed citations

19.

Farahani, Hossein & Jens Lagergren. (2013). Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming. PLoS ONE. 8(6). e65773–e65773. 27 indexed citations

20.

Farahani, Hossein, et al.. (2012). A-to-I editing of microRNAs in the mammalian brain increases during development. Genome Research. 22(8). 1477–1487. 91 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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