Ning‐I Yang

1.7k total citations
52 papers, 1.2k citations indexed

About

Ning‐I Yang is a scholar working on Cardiology and Cardiovascular Medicine, Surgery and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Ning‐I Yang has authored 52 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Cardiology and Cardiovascular Medicine, 16 papers in Surgery and 10 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Ning‐I Yang's work include Heart Failure Treatment and Management (10 papers), Cardiac Imaging and Diagnostics (9 papers) and Cardiovascular Function and Risk Factors (9 papers). Ning‐I Yang is often cited by papers focused on Heart Failure Treatment and Management (10 papers), Cardiac Imaging and Diagnostics (9 papers) and Cardiovascular Function and Risk Factors (9 papers). Ning‐I Yang collaborates with scholars based in Taiwan, United States and Japan. Ning‐I Yang's co-authors include Chao‐Hung Wang, Wen‐Jin Cherng, Ming‐Jui Hung, Chi‐Wen Cheng, Min‐Hui Liu, Li‐Tang Kuo, Yu-Yen Huang, Chun‐Tai Mao, Subodh Verma and Mei‐Ling Cheng and has published in prestigious journals such as Journal of the American College of Cardiology, Scientific Reports and Arteriosclerosis Thrombosis and Vascular Biology.

In The Last Decade

Ning‐I Yang

46 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ning‐I Yang Taiwan 19 586 420 212 165 161 52 1.2k
Mihály Ruppert Hungary 21 622 1.1× 360 0.9× 303 1.4× 64 0.4× 118 0.7× 72 1.2k
Cory R. Trankle United States 23 1.1k 1.8× 522 1.2× 401 1.9× 89 0.5× 147 0.9× 84 1.7k
Beatrice Musumeci Italy 23 1.3k 2.2× 415 1.0× 184 0.9× 200 1.2× 104 0.6× 129 1.8k
Akihiro Hirashiki Japan 19 594 1.0× 229 0.5× 197 0.9× 123 0.7× 155 1.0× 75 1.1k
Kenichi Matsushita Japan 18 464 0.8× 390 0.9× 244 1.2× 93 0.6× 146 0.9× 90 1.2k
Toshimitsu Nozaki Japan 14 869 1.5× 335 0.8× 313 1.5× 152 0.9× 151 0.9× 23 1.6k
Deddo Mörtl Austria 18 688 1.2× 221 0.5× 140 0.7× 49 0.3× 141 0.9× 28 1.2k
D. Ngo Australia 22 647 1.1× 281 0.7× 135 0.6× 131 0.8× 304 1.9× 80 1.4k
Keisuke Ohba Japan 15 946 1.6× 378 0.9× 344 1.6× 233 1.4× 127 0.8× 24 1.7k
Lidia Staszewsky Italy 28 1.6k 2.8× 623 1.5× 444 2.1× 182 1.1× 174 1.1× 78 2.7k

Countries citing papers authored by Ning‐I Yang

Since Specialization
Citations

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

Fields of papers citing papers by Ning‐I Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ning‐I Yang. 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 Ning‐I Yang. The network helps show where Ning‐I Yang may publish in the future.

Co-authorship network of co-authors of Ning‐I Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Ning‐I Yang. A scholar is included among the top collaborators of Ning‐I Yang 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 Ning‐I Yang. Ning‐I Yang 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.
3.
Tsai, Tsung-Hsien, Chun‐Hung Chen, Yi-Ju Chou, et al.. (2024). Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease. Computational and Structural Biotechnology Journal. 27. 278–286. 3 indexed citations
4.
Chen, Dong‐Yi, Ming‐Shyan Lin, Chao‐Yung Wang, et al.. (2024). Ticagrelor versus Adjusted‐Dose Prasugrel in Acute Coronary Syndrome with Percutaneous Coronary Intervention. Clinical Pharmacology & Therapeutics. 116(3). 747–756. 2 indexed citations
5.
Tsai, Tzu‐Hsien, et al.. (2023). Evaluating the applicability of ivabradine in acute heart failure. Clinical Cardiology. 47(1). e24206–e24206. 3 indexed citations
6.
Yang, Ning‐I, Li‐Tang Kuo, Chin‐Chan Lee, et al.. (2023). Associations of Three-Dimensional Anthropometric Body Surface Scanning Measurements and Coronary Artery Disease. Medicina. 59(3). 570–570.
7.
Lin, Ming‐Shyan, Tzu‐Hsien Tsai, Ning‐I Yang, et al.. (2023). Comparing angiotensin receptor–neprilysin inhibitors with sodium–glucose cotransporter 2 inhibitors for heart failure with diabetes mellitus. Diabetology & Metabolic Syndrome. 15(1). 8 indexed citations
8.
Chao, Tze‐Fan, Chih‐Hsiang Chang, Yu‐Sheng Lin, et al.. (2023). Cardiovascular and renal outcomes in patients with atrial fibrillation and stage 4–5 chronic kidney disease receiving direct oral anticoagulants: a multicenter retrospective cohort study. Journal of Thrombosis and Thrombolysis. 57(1). 89–100. 3 indexed citations
9.
Liao, Pei‐Ju, Yu‐Ching Lin, Ming‐Kuo Ting, et al.. (2021). Adverse body measurements are superior to sarcopenia-associated measurements in predicting chronic diseases. Scientific Reports. 11(1). 7749–7749. 3 indexed citations
10.
Pan, Heng‐Chih, et al.. (2018). Circulating Klotho levels can predict long-term macrovascular outcomes in type 2 diabetic patients. Atherosclerosis. 276. 83–90. 37 indexed citations
11.
Hung, Ming‐Jui, et al.. (2016). Layer-specific quantification of myocardial deformation in sepsis-induced Takotsubo cardiomyopathy. Medicine. 95(44). e5250–e5250. 8 indexed citations
12.
Cheng, Mei‐Ling, Chao‐Hung Wang, Ming‐Shi Shiao, et al.. (2015). Metabolic Disturbances Identified in Plasma Are Associated With Outcomes in Patients With Heart Failure. Journal of the American College of Cardiology. 65(15). 1509–1520. 241 indexed citations
13.
Mao, Chun‐Tai, Min‐Hui Liu, Kuang‐Hung Hsu, et al.. (2014). Effect of multidisciplinary disease management for hospitalized heart failure under a national health insurance programme. Journal of Cardiovascular Medicine. 16(9). 616–624. 31 indexed citations
14.
Chen, Shih‐Jen, Ning‐I Yang, Chi‐Wen Cheng, et al.. (2012). Impact of Definitions of Left Ventricular Hypertrophy on Left Ventricular Remodeling Findings in Patients with Predialysis Chronic Kidney Disease: An Echocardiographic Study. Zhōnghuá mínguó xīnzàngxué huì zázhì. 28(1). 42–52.
15.
Liu, Min‐Hui, Chao‐Hung Wang, Yu-Yen Huang, et al.. (2012). Edema index established by a segmental multifrequency bioelectrical impedance analysis provides prognostic value in acute heart failure. Journal of Cardiovascular Medicine. 13(5). 299–306. 37 indexed citations
16.
Chiu, Sherry Yueh‐Hsia, et al.. (2011). Correlation of Clinical Changes with Regard to Thyroxine Replacement Therapy in Hypothyroid Patients – Focusing on the Change of Renal Function. Kidney & Blood Pressure Research. 34(5). 365–372. 10 indexed citations
17.
Cheng, Chi‐Wen, et al.. (2010). Variant Angina with Angiographically Normal or Near-Normal Coronary Arteries: A 10-year Experience. 21(2). 79–89. 2 indexed citations
19.
Wang, Chao‐Hung, Ming‐Kuo Ting, Subodh Verma, et al.. (2006). Pioglitazone increases the numbers and improves the functional capacity of endothelial progenitor cells in patients with diabetes mellitus. American Heart Journal. 152(6). 1051.e1–1051.e8. 105 indexed citations
20.

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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