Lars J. Kangas

1.9k total citations
31 papers, 1.5k citations indexed

About

Lars J. Kangas is a scholar working on Spectroscopy, Molecular Biology and Control and Systems Engineering. According to data from OpenAlex, Lars J. Kangas has authored 31 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Spectroscopy, 7 papers in Molecular Biology and 7 papers in Control and Systems Engineering. Recurrent topics in Lars J. Kangas's work include Metabolomics and Mass Spectrometry Studies (7 papers), Advanced Proteomics Techniques and Applications (7 papers) and Mass Spectrometry Techniques and Applications (7 papers). Lars J. Kangas is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (7 papers), Advanced Proteomics Techniques and Applications (7 papers) and Mass Spectrometry Techniques and Applications (7 papers). Lars J. Kangas collaborates with scholars based in United States, Netherlands and Egypt. Lars J. Kangas's co-authors include Richard Smith, Konstantinos Pétritis, Eric F. Strittmatter, David Camp, Barbara Rasco, Yiqun Huang, Matthew Monroe, Jon Jacobs, Weijun Qian and Gordon Anderson and has published in prestigious journals such as Bioinformatics, Analytical Chemistry and Critical Reviews in Food Science and Nutrition.

In The Last Decade

Lars J. Kangas

29 papers receiving 1.4k citations

Peers

Lars J. Kangas

SPECT

RADIA

Guozhong Li China

Yuanyuan Wu China

Christopher D. Brown United States

Weiwei Hu China

Darren T. Andrews United Kingdom

Krzysztof Kaczmarek Poland

Zheng Rong Yang United Kingdom

Michael Schneider Germany

Evgeny Byvatov Germany

Kevin Yang United States

Guozhong Li China

Lars J. Kangas

921 ×1.2

649 ×0.9

SPECT

30 ×0.2

74 ×0.5

6 ×0.1

RADIA

Citations per year, relative to Lars J. Kangas Lars J. Kangas (= 1×) peers Guozhong Li

Countries citing papers authored by Lars J. Kangas

Since Specialization

Citations

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

Fields of papers citing papers by Lars J. Kangas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lars J. Kangas. 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 Lars J. Kangas. The network helps show where Lars J. Kangas may publish in the future.

Co-authorship network of co-authors of Lars J. Kangas

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

All Works

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20 of 20 papers shown

Kangas, Lars J.. (2023). Artificial neural network cardiopulmonary modeling and diagnosis. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

Miller, John H., et al.. (2014). Artificial Neural Network for Charge Prediction in Metabolite Identification by Mass Spectrometry. Methods in molecular biology. 1260. 89–100. 5 indexed citations

Kangas, Lars J., Thomas Metz, Giorgis Isaac, et al.. (2012). In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids. Bioinformatics. 28(13). 1705–1713. 57 indexed citations

Chin, George, et al.. (2011). Evaluating In-Clique and Topological Parallelism Strategies for Junction Tree-Based Bayesian Network Inference Algorithm on the Cray XMT. 16. 1710–1719. 2 indexed citations

Chin, George, et al.. (2010). Fault Detection in Distributed Climate Sensor Networks Using Dynamic Bayesian Networks. 50. 121–128. 11 indexed citations

Shah, Anuj, Khushbu Agarwal, Erin Baker, et al.. (2010). Machine learning based prediction for peptide drift times in ion mobility spectrometry. Bioinformatics. 26(13). 1601–1607. 34 indexed citations

Greitzer, Frank L., Patrick Paulson, Lars J. Kangas, et al.. (2009). Predictive Modeling for Insider Threat Mitigation. 10 indexed citations

Kangas, Lars J., Paul E. Keller, Edward R. Siciliano, R. T. Kouzes, & James H. Ely. (2008). The use of artificial neural networks in PVT-based radiation portal monitors. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 587(2-3). 398–412. 53 indexed citations

Huang, Yiqun, Lars J. Kangas, & Barbara Rasco. (2007). Applications of Artificial Neural Networks (ANNs) in Food Science. Critical Reviews in Food Science and Nutrition. 47(2). 113–126. 149 indexed citations

10.

Kangas, Lars J., et al.. (2005). Adaptive life simulator: a novel approach to modeling the cardiovascular system. 1. 796–800. 7 indexed citations

11.

Strittmatter, Eric F., Lars J. Kangas, Konstantinos Pétritis, et al.. (2004). Application of Peptide LC Retention Time Information in a Discriminant Function for Peptide Identification by Tandem Mass Spectrometry. Journal of Proteome Research. 3(4). 760–769. 123 indexed citations

12.

Varnum, Susan M., Ronald L. Woodbury, Konstantinos Pétritis, et al.. (2003). Proteomic Characterization of Nipple Aspirate Fluid: Identification of Potential Biomarkers of Breast Cancer. Breast Cancer Research and Treatment. 80(1). 87–97. 85 indexed citations

13.

Pétritis, Konstantinos, Lars J. Kangas, Patrick Ferguson, et al.. (2003). Use of Artificial Neural Networks for the Accurate Prediction of Peptide Liquid Chromatography Elution Times in Proteome Analyses. Analytical Chemistry. 75(5). 1039–1048. 253 indexed citations

14.

Kangas, Lars J., et al.. (2002). Alpha spectral analysis via artificial neural networks. 1. 418–421. 3 indexed citations

15.

Keller, Paul E., S. Hashem, Lars J. Kangas, & R. T. Kouzes. (1996). Applications of Neural Networks in Environment, Energy, and Health: Proceedings of the 1995 Workshop on Environmental and Energy Applications of Neural Networks, Pacific Northwest National Laboratory, Richland, Washington, U. S. A., 30-31 Mar.

16.

Keller, Paul E., S. Hashem, Lars J. Kangas, & R. T. Kouzes. (1996). Applications of Neural Networks in Environment, Energy, and Health. 1–240. 5 indexed citations

17.

Keller, Paul E., et al.. (1995). Nuclear spectral analysis via artificial neural networks for waste handling. IEEE Transactions on Nuclear Science. 42(4). 709–715. 35 indexed citations

18.

Keller, Paul E., R. T. Kouzes, & Lars J. Kangas. (1994). Three neural network based, sensor systems for environmental monitoring. 377–382. 27 indexed citations

19.

Kangas, Lars J., et al.. (1994). TEDANN: Turbine engine diagnostic artificial neural network. University of North Texas Digital Library (University of North Texas). 5 indexed citations

20.

Greitzer, Frank L., et al.. (1994). An artificial neural network system for diagnosing gas turbine engine fuel faults. University of North Texas Digital Library (University of North Texas). 6 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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