Lusha W. Liang

707 total citations
21 papers, 243 citations indexed

About

Lusha W. Liang is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology and Genetics. According to data from OpenAlex, Lusha W. Liang has authored 21 papers receiving a total of 243 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cardiology and Cardiovascular Medicine, 12 papers in Molecular Biology and 5 papers in Genetics. Recurrent topics in Lusha W. Liang's work include Cardiomyopathy and Myosin Studies (13 papers), Studies on Chitinases and Chitosanases (7 papers) and Galectins and Cancer Biology (3 papers). Lusha W. Liang is often cited by papers focused on Cardiomyopathy and Myosin Studies (13 papers), Studies on Chitinases and Chitosanases (7 papers) and Galectins and Cancer Biology (3 papers). Lusha W. Liang collaborates with scholars based in United States. Lusha W. Liang's co-authors include Han N. Lim, Razika Hussein, Yuichi J. Shimada, Muredach P. Reilly, Matthew J. Maurer, Kohei Hasegawa, Michael A. Fifer, Frederick S. Kaplan, Michael Fuery and Emile R. Mohler and has published in prestigious journals such as Nucleic Acids Research, Journal of the American College of Cardiology and Genetics.

In The Last Decade

Lusha W. Liang

18 papers receiving 242 citations

Peers

Lusha W. Liang
Cécile Montgiraud France
Ulrike Lisewski Germany
Carsten Tschöepe Germany
Nir Lewis Israel
Noura M. Dabbouseh United States
Jeffrey Schubert United States
Gillian Mellars United Kingdom
Vikas Ghai United States
A. Panek Poland
Lennart Suckau Germany
Cécile Montgiraud France
Lusha W. Liang
Citations per year, relative to Lusha W. Liang Lusha W. Liang (= 1×) peers Cécile Montgiraud

Countries citing papers authored by Lusha W. Liang

Since Specialization
Citations

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

Fields of papers citing papers by Lusha W. Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lusha W. Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Lusha W. Liang. A scholar is included among the top collaborators of Lusha W. 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 Lusha W. Liang. Lusha W. 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.
Lumish, Heidi, Lorenzo R. Sewanan, Lusha W. Liang, et al.. (2025). Comprehensive Plasma Proteomic Profiling Reveals Differentially Regulated Signaling Pathways Underlying Left Ventricular Hypertrophy Between Hypertrophic Cardiomyopathy and Aortic Stenosis. Journal of Cardiovascular Translational Research. 18(3). 650–659.
2.
Liang, Lusha W., et al.. (2024). 493 Knowledge of Familial Hypercholesterolemia Among Cardiology Healthcare Providers. Journal of Clinical and Translational Science. 8(s1). 146–146.
3.
Liang, Lusha W., Kohei Hasegawa, Mathew S. Maurer, et al.. (2024). Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using plasma proteomics profiling. EP Europace. 26(11).
4.
Liang, Lusha W., Heidi Lumish, Lorenzo R. Sewanan, et al.. (2024). Evolving Strategies for the Management of Obstructive Hypertrophic Cardiomyopathy. Journal of Cardiac Failure. 30(9). 1136–1153. 2 indexed citations
5.
Liang, Lusha W., Kohei Hasegawa, Matthew J. Maurer, et al.. (2023). Comprehensive Transcriptomics Profiling of MicroRNA Reveals Plasma Circulating Biomarkers of Hypertrophic Cardiomyopathy and Dysregulated Signaling Pathways. Circulation Heart Failure. 16(6). e010010–e010010. 7 indexed citations
6.
Lumish, Heidi, Lusha W. Liang, Kohei Hasegawa, et al.. (2023). Prediction of worsening heart failure in hypertrophic cardiomyopathy using plasma proteomics. Heart. 109(24). 1837–1843. 7 indexed citations
7.
Liang, Lusha W., et al.. (2023). Patient experiences in receiving telegenetics care for inherited cardiovascular diseases. Journal of Community Genetics. 15(2). 119–127. 1 indexed citations
8.
Liang, Lusha W., Kohei Hasegawa, Matthew J. Maurer, et al.. (2023). Signaling Pathways Associated With Prior Cardiovascular Events in Hypertrophic Cardiomyopathy. Journal of Cardiac Failure. 30(3). 462–472. 4 indexed citations
9.
Liang, Lusha W., Heidi Lumish, Lorenzo R. Sewanan, et al.. (2023). Advanced Heart Failure Therapies for Hypertrophic Cardiomyopathy. JACC Heart Failure. 11(11). 1473–1480. 8 indexed citations
10.
Shimada, Yuichi J., Yoshihiko Raita, Lusha W. Liang, et al.. (2022). Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling. Circulation Genomic and Precision Medicine. 15(6). e003546–e003546. 8 indexed citations
11.
Lumish, Heidi, Lusha W. Liang, Matthew Maurer, et al.. (2022). Prediction of worsening heart failure in patients with hypertrophic cardiomyopathy using plasma proteomics profiling. European Heart Journal. 43(Supplement_2). 1 indexed citations
12.
Liang, Lusha W., Yoshihiko Raita, Kohei Hasegawa, et al.. (2022). Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy. Heart. 108(22). 1807–1814. 12 indexed citations
13.
Shimada, Yuichi J., Yoshihiko Raita, Lusha W. Liang, et al.. (2021). Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy. Circulation Heart Failure. 14(7). e007849–e007849. 38 indexed citations
14.
Liang, Lusha W., Michael A. Fifer, Kohei Hasegawa, et al.. (2021). Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning. Circulation Genomic and Precision Medicine. 14(3). e003259–e003259. 11 indexed citations
15.
Liang, Lusha W., et al.. (2021). IMPROVED PREDICTION OF GENOTYPE POSITIVITY USING MACHINE LEARNING IN PATIENTS WITH HYPERTROPHIC CARDIOMYOPATHY. Journal of the American College of Cardiology. 77(18). 533–533. 1 indexed citations
16.
Shimada, Yuichi J., Yoshihiko Raita, Lusha W. Liang, et al.. (2021). Comprehensive proteomics profiling reveals molecular pathways that are differentially regulated in hypertrophic cardiomyopathy and correlate with clinical markers of disease severity. European Heart Journal. 42(Supplement_1). 1 indexed citations
17.
Fuery, Michael, Lusha W. Liang, Frederick S. Kaplan, & Emile R. Mohler. (2017). Vascular ossification: Pathology, mechanisms, and clinical implications. Bone. 109. 28–34. 36 indexed citations
18.
Liang, Lusha W., Alexendar R. Perez, Qin Zhou, et al.. (2016). An Assessment of Prognostic Factors, Adjuvant Treatment, and Outcomes of Stage IA Polyp-Limited Versus Endometrium-Limited Type II Endometrial Carcinoma. International Journal of Gynecological Cancer. 26(3). 497–504. 13 indexed citations
19.
Hussein, Razika, et al.. (2012). Regulatory consequences of gene translocation in bacteria. Nucleic Acids Research. 40(18). 8979–8992. 79 indexed citations
20.
Liang, Lusha W., et al.. (2012). Minimal Effect of Gene Clustering on Expression inEscherichia coli. Genetics. 193(2). 453–465. 8 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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