James Liang

842 total citations
20 papers, 370 citations indexed

About

James Liang is a scholar working on Genetics, Hematology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, James Liang has authored 20 papers receiving a total of 370 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Genetics, 7 papers in Hematology and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in James Liang's work include Myeloproliferative Neoplasms: Diagnosis and Treatment (5 papers), Acute Myeloid Leukemia Research (3 papers) and Multiple Myeloma Research and Treatments (3 papers). James Liang is often cited by papers focused on Myeloproliferative Neoplasms: Diagnosis and Treatment (5 papers), Acute Myeloid Leukemia Research (3 papers) and Multiple Myeloma Research and Treatments (3 papers). James Liang collaborates with scholars based in United States, Australia and New Zealand. James Liang's co-authors include Jonathan B. Rothbard, Kwok M. Ho, Jeremy Nayagam, Anand M. Gautam, Lars Fugger, Hugh O. McDevitt, Dongfang Liu, Amina S. Woods, Kate J. Wilson and Dennis M. Zaller and has published in prestigious journals such as Blood, The Journal of Immunology and IEEE Transactions on Image Processing.

In The Last Decade

James Liang

19 papers receiving 360 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Liang United States 11 110 88 66 58 46 20 370
Oscar Persson Sweden 6 182 1.7× 95 1.1× 117 1.8× 13 0.2× 31 0.7× 10 406
Chuanyin Sun China 12 131 1.2× 72 0.8× 25 0.4× 20 0.3× 17 0.4× 25 378
Siyuan Yang China 11 28 0.3× 68 0.8× 31 0.5× 140 2.4× 95 2.1× 35 331
Nicolás González United States 9 28 0.3× 22 0.3× 115 1.7× 23 0.4× 55 1.2× 27 293
Yandong Shen Australia 11 60 0.5× 79 0.9× 82 1.2× 58 1.0× 7 0.2× 29 396
Jie Bai China 13 65 0.6× 95 1.1× 81 1.2× 169 2.9× 18 0.4× 51 604
M. Ohta Japan 13 40 0.4× 128 1.5× 127 1.9× 8 0.1× 26 0.6× 20 414
Wen Zhu China 15 26 0.2× 104 1.2× 239 3.6× 108 1.9× 49 1.1× 37 651
Ying Su China 8 23 0.2× 96 1.1× 73 1.1× 121 2.1× 17 0.4× 22 500
Kamilia Rizkalla Canada 11 18 0.2× 48 0.5× 27 0.4× 29 0.5× 33 0.7× 32 314

Countries citing papers authored by James Liang

Since Specialization
Citations

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

Fields of papers citing papers by James Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Liang

This figure shows the co-authorship network connecting the top 25 collaborators of James Liang. A scholar is included among the top collaborators of James Liang 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 James Liang. James Liang 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.
Liu, Xiang, James Liang, Taige Chen, et al.. (2025). Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction. Computer Methods and Programs in Biomedicine. 265. 108705–108705. 2 indexed citations
2.
Gill, Harinder, Francesca Palandri, David M. Ross, et al.. (2023). Bomedemstat (IMG-7289), an LSD1 Inhibitor, Manages the Signs and Symptoms of Essential Thrombocythemia (ET) While Reducing the Burden of Cells Homozygous for Driver Mutations. Blood. 142(Supplement 1). 747–747. 4 indexed citations
3.
Liu, Dongfang, et al.. (2023). Tripartite Feature Enhanced Pyramid Network for Dense Prediction. IEEE Transactions on Image Processing. 32. 2678–2692. 50 indexed citations
4.
Huang, Nen‐Fu, et al.. (2022). Proficiency Prediction System for Online Learning Based on Recurrent Neural Networks. 74–78. 1 indexed citations
5.
Liang, James, Yuxing Wang, Yingjie Chen, Baijian Yang, & Dongfang Liu. (2022). A Triangulation-Based Visual Localization for Field Robots. IEEE/CAA Journal of Automatica Sinica. 9(6). 1083–1086. 12 indexed citations
6.
Ruskova, Anna, et al.. (2019). Pure erythroid leukemia: The value of E‐cadherin in making the diagnosis. American Journal of Hematology. 94(6). 726–727. 3 indexed citations
7.
Guo, Belinda B., Matthew D. Linden, Kathryn A. Fuller, et al.. (2019). Platelets in myeloproliferative neoplasms have a distinct transcript signature in the presence of marrow fibrosis. British Journal of Haematology. 188(2). 272–282. 16 indexed citations
8.
Liang, James, Kathryn A. Fuller, Catherine Cole, et al.. (2018). Automated enumeration of lymphoid and plasma cells in bone marrow to establish normal reference ranges. Journal of Clinical Pathology. 71(10). 916–925. 7 indexed citations
9.
Hui, Henry, Kathryn A. Fuller, James Liang, et al.. (2017). Imaging flow cytometry to assess chromosomal abnormalities in chronic lymphocytic leukaemia. Methods. 134-135. 32–40. 20 indexed citations
10.
Guo, Belinda B., Richard J. N. Allcock, Fizzah Choudry, et al.. (2017). Megakaryocytes in Myeloproliferative Neoplasms Have Unique Somatic Mutations. American Journal Of Pathology. 187(7). 1512–1522. 14 indexed citations
11.
Zhou, Zhiming, Xin Xu, James Liang, et al.. (2016). [The impact of cigarette cessation intervention on mental state of patients with coronary heart disease].. PubMed. 55(11). 854–858. 1 indexed citations
12.
Kimura, Nobuhiko, et al.. (2015). Adult Intussusception Secondary to Inflammatory Fibroid Polyp. Western Journal of Emergency Medicine. 16(4). 581–582. 10 indexed citations
13.
McCulloch, Rory, et al.. (2013). Weekly Subcutaneous Bortezomib Is Well Tolerated and Effective As Initial Therapy Of Symptomatic Myeloma. Blood. 122(21). 3229–3229. 3 indexed citations
14.
Liang, James, Adam Field, & Kimberly P. Liang. (2013). Bronchopleural Fistual. Western Journal of Emergency Medicine. 14(5). 409–410.
15.
16.
Peng, Philip, Michael J. Wiley, James Liang, & Geoff Bellingham. (2010). Ultrasound-guided suprascapular nerve block: a correlation with fluoroscopic and cadaveric findings. Canadian Journal of Anesthesia/Journal canadien d anesthésie. 57(2). 143–148. 41 indexed citations
17.
Liang, James, et al.. (2007). Alkylated Naphthalenes as High-Performance Synthetic Lubricating Fluids. Tribology Transactions. 50(1). 82–87. 25 indexed citations
18.
Nayagam, Jeremy, Kwok M. Ho, & James Liang. (2004). Fatal Systemic Air Embolism during Endoscopic Retrograde Cholangio-pancreatography. Anaesthesia and Intensive Care. 32(2). 260–264. 38 indexed citations
19.
Fugger, Lars, James Liang, Anand M. Gautam, Jonathan B. Rothbard, & Hugh O. McDevitt. (1996). Quantitative Analysis of Peptides from Myelin Basic Protein Binding to the MHC Class II Protein, I-Au, Which Confers Susceptibility to Experimental Allergic Encephalomyelitis. Molecular Medicine. 2(2). 181–188. 58 indexed citations
20.
Marshall, K. Wayne, Kate J. Wilson, James Liang, et al.. (1995). Prediction of peptide affinity to HLA DRB1*0401.. The Journal of Immunology. 154(11). 5927–5933. 64 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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