Daniel P. Howsmon

1.4k total citations
32 papers, 965 citations indexed

About

Daniel P. Howsmon is a scholar working on Genetics, Surgery and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Daniel P. Howsmon has authored 32 papers receiving a total of 965 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Genetics, 10 papers in Surgery and 8 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Daniel P. Howsmon's work include Diabetes Management and Research (8 papers), Pancreatic function and diabetes (8 papers) and Cardiac Valve Diseases and Treatments (7 papers). Daniel P. Howsmon is often cited by papers focused on Diabetes Management and Research (8 papers), Pancreatic function and diabetes (8 papers) and Cardiac Valve Diseases and Treatments (7 papers). Daniel P. Howsmon collaborates with scholars based in United States and United Kingdom. Daniel P. Howsmon's co-authors include Juergen Hahn, James B. Adams, Nancy Isern, Catherine Lozupone, B. Wayne Bequette, David Hoyt, Rosa Krajmalnik‐Brown, Michael Shaffer, Dae‐Wook Kang and Zehra Esra Ilhan and has published in prestigious journals such as Journal of Biological Chemistry, PLoS ONE and Diabetes Care.

In The Last Decade

Daniel P. Howsmon

32 papers receiving 952 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel P. Howsmon United States 15 345 257 243 231 217 32 965
Tianqi Wang China 16 393 1.1× 154 0.6× 148 0.6× 63 0.3× 32 0.1× 87 1.0k
Nannan Li China 21 311 0.9× 60 0.2× 133 0.5× 81 0.4× 35 0.2× 100 1.4k
Jussi Korpela Finland 18 163 0.5× 68 0.3× 268 1.1× 80 0.3× 44 0.2× 35 974
Wenfei Wang China 22 713 2.1× 116 0.5× 45 0.2× 181 0.8× 50 0.2× 109 1.6k
Mei Ma China 15 324 0.9× 147 0.6× 46 0.2× 36 0.2× 19 0.1× 86 996
Kunio Kasugai Japan 24 480 1.4× 89 0.3× 21 0.1× 759 3.3× 77 0.4× 158 2.2k
Congcong Li China 21 280 0.8× 24 0.1× 125 0.5× 135 0.6× 64 0.3× 79 1.2k
Sungji Ha South Korea 14 194 0.6× 136 0.5× 242 1.0× 40 0.2× 40 0.2× 31 697
Seong Hee Choi South Korea 21 182 0.5× 71 0.3× 38 0.2× 192 0.8× 28 0.1× 122 1.3k
Tsutomu Kamei Japan 15 189 0.5× 106 0.4× 114 0.5× 56 0.2× 30 0.1× 56 971

Countries citing papers authored by Daniel P. Howsmon

Since Specialization
Citations

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

Fields of papers citing papers by Daniel P. Howsmon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel P. Howsmon. 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 Daniel P. Howsmon. The network helps show where Daniel P. Howsmon may publish in the future.

Co-authorship network of co-authors of Daniel P. Howsmon

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel P. Howsmon. A scholar is included among the top collaborators of Daniel P. Howsmon 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 Daniel P. Howsmon. Daniel P. Howsmon 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.
Howsmon, Daniel P., et al.. (2024). Statistical process monitoring creates a hemodynamic trajectory map after pediatric cardiac surgery: A case study of the arterial switch operation. Bioengineering & Translational Medicine. 9(6). e10679–e10679. 1 indexed citations
2.
West, Toni M., et al.. (2023). The effects of strain history on aortic valve interstitial cell activation in a 3D hydrogel environment. APL Bioengineering. 7(2). 26101–26101. 1 indexed citations
3.
Feng, Xinzeng, et al.. (2022). Three-dimensional analysis of hydrogel-imbedded aortic valve interstitial cell shape and its relation to contractile behavior. Acta Biomaterialia. 163. 194–209. 10 indexed citations
4.
Nichols, Eva-Maria, et al.. (2022). Mathematical Modeling of Complement Pathway Dynamics for Target Validation and Selection of Drug Modalities for Complement Therapies. Frontiers in Pharmacology. 13. 855743–855743. 7 indexed citations
5.
Castillero, Estíbaliz, Daniel P. Howsmon, Bruno V. Rego, et al.. (2021). Altered Responsiveness to TGFβ and BMP and Increased CD45+ Cell Presence in Mitral Valves Are Unique Features of Ischemic Mitral Regurgitation. Arteriosclerosis Thrombosis and Vascular Biology. 41(6). 2049–2062. 2 indexed citations
6.
Howsmon, Daniel P. & Michael S. Sacks. (2021). On Valve Interstitial Cell Signaling: The Link Between Multiscale Mechanics and Mechanobiology. Cardiovascular Engineering and Technology. 12(1). 15–27. 7 indexed citations
7.
Howsmon, Daniel P., Bruno V. Rego, Estíbaliz Castillero, et al.. (2020). Mitral valve leaflet response to ischaemic mitral regurgitation: from gene expression to tissue remodelling. Journal of The Royal Society Interface. 17(166). 20200098–20200098. 20 indexed citations
8.
Ayoub, Salma, Daniel P. Howsmon, Chung‐Hao Lee, & Michael S. Sacks. (2020). On the role of predicted in vivo mitral valve interstitial cell deformation on its biosynthetic behavior. Biomechanics and Modeling in Mechanobiology. 20(1). 135–144. 10 indexed citations
9.
Howsmon, Daniel P., et al.. (2018). Kinesin-2 heterodimerization alters entry into a processive run along the microtubule but not stepping within the run. Journal of Biological Chemistry. 293(35). 13389–13400. 4 indexed citations
10.
Howsmon, Daniel P., James B. Adams, Uwe Krüger, et al.. (2018). Erythrocyte fatty acid profiles in children are not predictive of autism spectrum disorder status: a case control study. Biomarker Research. 6(1). 12–12. 9 indexed citations
11.
Forlenza, Gregory P., Faye Cameron, Trang T. Ly, et al.. (2018). Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting. Diabetes Technology & Therapeutics. 20(5). 335–343. 59 indexed citations
12.
Vargason, Troy, Daniel P. Howsmon, Deborah L. McGuinness, & Juergen Hahn. (2017). On the Use of Multivariate Methods for Analysis of Data from Biological Networks. Processes. 5(3). 36–36. 14 indexed citations
13.
Kang, Dae‐Wook, Zehra Esra Ilhan, Nancy Isern, et al.. (2017). Differences in fecal microbial metabolites and microbiota of children with autism spectrum disorders. Anaerobe. 49. 121–131. 265 indexed citations
14.
Adams, James B., Daniel P. Howsmon, Uwe Krüger, et al.. (2017). Significant Association of Urinary Toxic Metals and Autism-Related Symptoms—A Nonlinear Statistical Analysis with Cross Validation. PLoS ONE. 12(1). e0169526–e0169526. 32 indexed citations
15.
Cameron, Faye, Trang T. Ly, Bruce A. Buckingham, et al.. (2017). Closed-Loop Control Without Meal Announcement in Type 1 Diabetes. Diabetes Technology & Therapeutics. 19(9). 527–532. 75 indexed citations
16.
Howsmon, Daniel P., Uwe Krüger, Stepan Melnyk, S. Jill James, & Juergen Hahn. (2017). Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation. PLoS Computational Biology. 13(3). e1005385–e1005385. 90 indexed citations
17.
Forlenza, Gregory P., Sunil Deshpande, Trang T. Ly, et al.. (2017). Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial. Diabetes Care. 40(8). 1096–1102. 39 indexed citations
18.
Howsmon, Daniel P. & Juergen Hahn. (2016). Regularization Techniques to Overcome Overparameterization of Complex Biochemical Reaction Networks. PubMed. 2(3). 31–34. 9 indexed citations
19.
Howsmon, Daniel P., Boliang Zhang, Juergen Hahn, et al.. (2015). Entity linking for biomedical literature. BMC Medical Informatics and Decision Making. 15(S1). S4–S4. 42 indexed citations
20.
Klemashevich, Cory, et al.. (2013). Rational identification of diet-derived postbiotics for improving intestinal microbiota function. Current Opinion in Biotechnology. 26. 85–90. 72 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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