Patrick Sin‐Chan

1.9k total citations
7 papers, 65 citations indexed

About

Patrick Sin‐Chan is a scholar working on Cancer Research, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Patrick Sin‐Chan has authored 7 papers receiving a total of 65 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Cancer Research, 2 papers in Molecular Biology and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Patrick Sin‐Chan's work include Radiomics and Machine Learning in Medical Imaging (2 papers), Chromatin Remodeling and Cancer (2 papers) and MicroRNA in disease regulation (2 papers). Patrick Sin‐Chan is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (2 papers), Chromatin Remodeling and Cancer (2 papers) and MicroRNA in disease regulation (2 papers). Patrick Sin‐Chan collaborates with scholars based in United States, Canada and Netherlands. Patrick Sin‐Chan's co-authors include Annie Huang, Jennifer A. Chan, Adriana Fonseca, Ben Ho, Christian Perotti, J. Gregory Cairncross, Aru Narendran, Cynthia Hawkins, Douglas Strother and Lucie Lafay‐Cousin and has published in prestigious journals such as Journal of Clinical Oncology, Aging Cell and Neuro-Oncology.

In The Last Decade

Patrick Sin‐Chan

7 papers receiving 64 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Patrick Sin‐Chan United States 4 39 27 16 14 11 7 65
Sebastian Brabetz Germany 4 56 1.4× 28 1.0× 20 1.3× 22 1.6× 6 0.5× 6 80
Byungjin Kim Canada 2 39 1.0× 40 1.5× 12 0.8× 21 1.5× 9 0.8× 3 69
Lee Schalop United States 2 34 0.9× 41 1.5× 22 1.4× 14 1.0× 9 0.8× 2 62
Clare Devlin United States 4 16 0.4× 17 0.6× 15 0.9× 5 0.4× 7 0.6× 9 54
Adam Banda United States 2 33 0.8× 46 1.7× 27 1.7× 11 0.8× 10 0.9× 3 77
Larissa Arning Germany 3 26 0.7× 10 0.4× 14 0.9× 7 0.5× 5 0.5× 3 55
Mirjam Blattner-Johnson Germany 3 24 0.6× 7 0.3× 13 0.8× 19 1.4× 10 0.9× 4 59
Mario Loehr Germany 4 26 0.7× 11 0.4× 12 0.8× 9 0.6× 13 1.2× 5 62
Fatimah Alqubaishi Saudi Arabia 4 17 0.4× 13 0.5× 7 0.4× 9 0.6× 5 0.5× 6 45
Andrew Guajardo United States 2 25 0.6× 34 1.3× 7 0.4× 9 0.6× 32 2.9× 4 69

Countries citing papers authored by Patrick Sin‐Chan

Since Specialization
Citations

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

Fields of papers citing papers by Patrick Sin‐Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Patrick Sin‐Chan. 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 Patrick Sin‐Chan. The network helps show where Patrick Sin‐Chan may publish in the future.

Co-authorship network of co-authors of Patrick Sin‐Chan

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

All Works

7 of 7 papers shown
1.
Schmauch, Benoît, Sarah Elsoukkary, Amika Moro, et al.. (2023). Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery. Journal of Pathology Informatics. 15. 100360–100360. 6 indexed citations
2.
Schmauch, Benoît, Sarah McIntyre, Patrick Sin‐Chan, et al.. (2023). Machine learning-based multimodal prediction of prognosis in patients with resected intrahepatic cholangiocarcinoma.. Journal of Clinical Oncology. 41(16_suppl). 4121–4121. 1 indexed citations
3.
Hartman, Douglas J., Ye Ye, Yaming Li, et al.. (2022). Application of deep learning models on whole slide images uncover new histological markers related to high-risk malignant pleural mesothelioma.. Journal of Clinical Oncology. 40(16_suppl). e13580–e13580. 1 indexed citations
4.
Lidzbarsky, Gabriel, Sofiya Milman, Tina Gao, et al.. (2020). Similar burden of pathogenic coding variants in exceptionally long‐lived individuals and individuals without exceptional longevity. Aging Cell. 19(10). e13216–e13216. 7 indexed citations
5.
Sin‐Chan, Patrick, Bryan Li, Ben Ho, Adriana Fonseca, & Annie Huang. (2018). Molecular Classification and Management of Rare Pediatric Embryonal Brain Tumors. Current Oncology Reports. 20(9). 69–69. 12 indexed citations
6.
Dixit, Rajiv, et al.. (2016). PDTB-22. THE ROLE OF THE CHROMOSOME 19 microRNA CLUSTER (C19MC) IN EMBRYONAL TUMOR WITH MULTILAYERED ROSETTES (ETMR). Neuro-Oncology. 18(suppl_6). vi154–vi154. 1 indexed citations
7.
Spence, Tara, Christian Perotti, Patrick Sin‐Chan, et al.. (2013). A novel C19MC amplified cell line links Lin28/let-7 to mTOR signaling in embryonal tumor with multilayered rosettes. Neuro-Oncology. 16(1). 62–71. 37 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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2026