Sarah J. MacEachern

24 papers receiving 842 citations

Sarah J. MacEachern's Hit Papers

Machine learning for precision medicine 2020 · 304 citations
3040+2+4Years since publication100200300

Peers

Sarah J. MacEachern
Comparison fields: 5 of 127
  • Gastroenterology 238
  • Health Informatics 34
  • Biological Psychiatry 30
  • Pharmacy 40
  • Sensory Systems 35
Replace Carolina Malagelada with:
Carolina Malagelada Spain
Isabel Silva Portugal
Long Qian China
Maosheng Xu China
Chun-Hung Chang Taiwan
Haibo Yu China
Xiaoyin Wu China
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Citations per field
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Citations per year

Countries citing papers authored by Sarah J. MacEachern

Since Specialization
Citations

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

Fields of papers citing papers by Sarah J. MacEachern

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Sarah J. MacEachern, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sarah J. MacEachern Line = papers co-authored together Sarah J. MacEachern links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Machine learning for precision medicine
Hit paper breakdown →
2020304
2 2012232
3 201556
4 201151
5 202038
6 201531
7 201830
8 201623
9 201914
10 202014
11 202114
12 201810
13 20199
14 20187
15 20176
16 20245
17 20233
18 20253
19 20243
20 20222

About Sarah J. MacEachern

Sarah J. MacEachern is a scholar working on Psychiatry and Mental health, Pediatrics, Perinatology and Child Health, Clinical Psychology, Molecular Biology and Endocrine and Autonomic Systems, having authored 24 papers that have together received 859 indexed citations. Recurring topics across this work include Epilepsy research and treatment (2 papers), Family and Disability Support Research (2 papers), Gastrointestinal motility and disorders (2 papers), Migraine and Headache Studies (2 papers), Diet and metabolism studies (1 paper), Circadian rhythm and melatonin (1 paper), Autism Spectrum Disorder Research (1 paper) and Ion Transport and Channel Regulation (1 paper). The work is most often cited by research in Gastroenterology (238 citations), Health Informatics (34 citations), Biological Psychiatry (30 citations), Pharmacy (40 citations) and Sensory Systems (35 citations). Sarah J. MacEachern has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Nils D. Forkert, Keith A. Sharkey, Bhavik Anil Patel, Peter L. Moses, Greg M. Swain, Elice M. Brooks, Karl R. Tyler, Onesmo B. Balemba, Jill M. Hoffman and Zhao Hong. Their work appears in journals such as Gastroenterology, Clinical Neuroradiology, Frontiers in Pediatrics, Frontiers in Public Health and Journal of Molecular Medicine.

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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