Emma Schwager

7.5k total citations · 1 hit paper
18 papers, 2.2k citations indexed

About

Emma Schwager is a scholar working on Molecular Biology, Critical Care and Intensive Care Medicine and Epidemiology. According to data from OpenAlex, Emma Schwager has authored 18 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Critical Care and Intensive Care Medicine and 6 papers in Epidemiology. Recurrent topics in Emma Schwager's work include Gut microbiota and health (6 papers), Metabolomics and Mass Spectrometry Studies (5 papers) and Sepsis Diagnosis and Treatment (5 papers). Emma Schwager is often cited by papers focused on Gut microbiota and health (6 papers), Metabolomics and Mass Spectrometry Studies (5 papers) and Sepsis Diagnosis and Treatment (5 papers). Emma Schwager collaborates with scholars based in United States, New Zealand and Germany. Emma Schwager's co-authors include Curtis Huttenhower, Boyu Ren, Himel Mallick, Eric A. Franzosa, Levi Waldron, Siyuan Ma, Timothy L. Tickle, Ali Rahnavard, Lauren J. McIver and George Weingart and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nature Biotechnology and Bioinformatics.

In The Last Decade

Emma Schwager

17 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Multivariable association discovery in population-scale meta-omics studies

2021 1.3k citations Himel Mallick, Ali Rahnavard et al. PLoS Computational Biology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Emma Schwager United States	11	1.5k	394	283	256	207	18	2.2k
Himel Mallick United States	17	1.7k 1.1×	426 1.1×	300 1.1×	247 1.0×	232 1.1×	36	2.5k
Jeremy E. Wilkinson United States	18	1.2k 0.8×	393 1.0×	228 0.8×	191 0.7×	142 0.7×	31	2.0k
Siyuan Ma United States	13	1.3k 0.8×	331 0.8×	243 0.9×	207 0.8×	147 0.7×	29	2.1k
Ayshwarya Subramanian United States	12	1.8k 1.2×	419 1.1×	230 0.8×	238 0.9×	144 0.7×	16	2.8k
Mi Young Lim South Korea	26	1.4k 0.9×	465 1.2×	433 1.5×	200 0.8×	272 1.3×	54	2.1k
Arvind Venkataraman United States	17	1.5k 1.0×	419 1.1×	260 0.9×	286 1.1×	252 1.2×	26	2.7k
Evgeni Levin Netherlands	21	1.4k 0.9×	624 1.6×	317 1.1×	302 1.2×	180 0.9×	59	2.3k
Till Robin Lesker Germany	16	1.6k 1.0×	340 0.9×	448 1.6×	240 0.9×	363 1.8×	27	2.5k
Lingjing Jiang United States	17	1.2k 0.8×	437 1.1×	191 0.7×	164 0.6×	183 0.9×	32	2.0k
Damian R. Plichta United States	21	1.4k 0.9×	329 0.8×	316 1.1×	208 0.8×	227 1.1×	31	2.2k

Countries citing papers authored by Emma Schwager

Since Specialization

Citations

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

Fields of papers citing papers by Emma Schwager

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emma Schwager

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

All Works

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18 of 18 papers shown

Schwager, Emma, et al.. (2024). Machine learning modelling for predicting the utilization of invasive and non‐invasive ventilation throughout the ICU duration. Healthcare Technology Letters. 11(4). 252–257. 1 indexed citations

Myers, Laura C., Nicholas A. Bosch, Kathleen A. Daly, et al.. (2024). Opioid Administration Practice Patterns in Patients With Acute Respiratory Failure Who Undergo Invasive Mechanical Ventilation. Critical Care Explorations. 6(7). e1123–e1123.

Schwager, Emma, et al.. (2023). Machine learning prediction of the total duration of invasive and non-invasive ventilation During ICU Stay. SHILAP Revista de lepidopterología. 2(9). e0000289–e0000289. 4 indexed citations

Khanna, Ashish K., Takahiro Kinoshita, Annamalai Natarajan, et al.. (2023). Association of systolic, diastolic, mean, and pulse pressure with morbidity and mortality in septic ICU patients: a nationwide observational study. Annals of Intensive Care. 13(1). 9–9. 20 indexed citations

Schwager, Emma, Erina Ghosh, Larry J. Eshelman, et al.. (2023). Accurate and interpretable prediction of ICU-acquired AKI. Journal of Critical Care. 75. 154278–154278. 8 indexed citations

Ghazi, Andrew R., Ali Rahnavard, Eric A. Franzosa, et al.. (2022). High-sensitivity pattern discovery in large, paired multiomic datasets. Bioinformatics. 38(Supplement_1). i378–i385. 35 indexed citations

Schwager, Emma, Erina Ghosh, Larry J. Eshelman, et al.. (2021). Including urinary output to define AKI enhances the performance of machine learning models to predict AKI at admission. Journal of Critical Care. 62. 283–288. 4 indexed citations

Ma, Siyuan, Boyu Ren, Himel Mallick, et al.. (2021). A statistical model for describing and simulating microbial community profiles. PLoS Computational Biology. 17(9). e1008913–e1008913. 40 indexed citations

Schwager, Emma, Sonja Schiffer, Yale Chang, et al.. (2021). Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome. npj Digital Medicine. 4(1). 133–133. 19 indexed citations

10.

Mallick, Himel, Ali Rahnavard, Lauren J. McIver, et al.. (2021). Multivariable association discovery in population-scale meta-omics studies. PLoS Computational Biology. 17(11). e1009442–e1009442. 1302 indexed citations breakdown →

11.

Ghosh, Erina, Larry J. Eshelman, Emma Schwager, et al.. (2021). Estimation of Baseline Serum Creatinine with Machine Learning. American Journal of Nephrology. 52(9). 753–762. 5 indexed citations

12.

Shawwa, Khaled, et al.. (2020). Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning. Clinical Kidney Journal. 14(5). 1428–1435. 25 indexed citations

13.

Schwager, Emma, Himel Mallick, Steffen Ventz, & Curtis Huttenhower. (2017). A Bayesian method for detecting pairwise associations in compositional data. PLoS Computational Biology. 13(11). e1005852–e1005852. 32 indexed citations

14.

Sinha, Rashmi, Galeb Abu-Ali, Emily Vogtmann, et al.. (2017). Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nature Biotechnology. 35(11). 1077–1086. 314 indexed citations

15.

Yasuda, Koji, Tiffany Hsu, Carey Ann Gallini, et al.. (2017). Fluoride Depletes Acidogenic Taxa in Oral but Not Gut Microbial Communities in Mice. mSystems. 2(4). 22 indexed citations

16.

Schwager, Emma, Chengwei Luo, Curtis Huttenhower, & Xochitl C. Morgan. (2015). Genomic Sequencing and Other Tools for Studying Microbial Communities. Microbe Magazine. 10(10). 419–425. 2 indexed citations

17.

Tong, Maomeng, Ian McHardy, Paul Ruegger, et al.. (2014). Reprograming of gut microbiome energy metabolism by the FUT2 Crohn’s disease risk polymorphism. The ISME Journal. 8(11). 2193–2206. 159 indexed citations

18.

McHardy, Ian, Maryam Goudarzi, Maomeng Tong, et al.. (2013). Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome. 1(1). 17–17. 225 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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