James Lowey

1.4k citations
12 papers · 837 · h-index 9

Impact in

Papers in

    • Gene expression and cancer classification 7
    • Bioinformatics and Genomic Networks 4
    • Machine Learning in Bioinformatics 2
    • Single-cell and spatial transcriptomics 1
    • Machine Learning and Data Classification 2

James Lowey

11 papers receiving 799 citations

Peers

James Lowey
Comparison fields: 5 of 142
  • Artificial Intelligence 249
  • Cancer Research 82
  • Molecular Biology 322
  • Signal Processing 43
  • Pulmonary and Respiratory Medicine 121
Replace Nan Rosemary Ke with:
Nan Rosemary Ke United States
Luis Rueda Canada
Haohan Wang United States
Kyung-Ah Sohn South Korea
Yunsheng Liu China
Martin Slawski United States
Colin Molter Japan
Ryan J. Urbanowicz United States
Juan Liu China
Vanathi Gopalakrishnan United States
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Citations per field
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Citations per year

Countries citing papers authored by James Lowey

Since Specialization
Citations

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

Fields of papers citing papers by James Lowey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside James Lowey, 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 James Lowey Line = papers co-authored together James Lowey links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 2004320
2 2006155
3 2010134
4 200571
5 201063
6 200539
7 200618
8 200616
9 200615
10 20203
11 20203
12 20060

About James Lowey

James Lowey is a scholar working on Molecular Biology, Artificial Intelligence, Cancer Research, Genetics and Computer Networks and Communications, having authored 12 papers that have together received 837 indexed citations. Recurring topics across this work include Gene expression and cancer classification (7 papers), Bioinformatics and Genomic Networks (4 papers), Machine Learning in Bioinformatics (2 papers), Cancer Genomics and Diagnostics (2 papers), Machine Learning and Data Classification (2 papers), Single-cell and spatial transcriptomics (1 paper), Prostate Cancer Treatment and Research (1 paper) and Genetic and phenotypic traits in livestock (1 paper). The work is most often cited by research in Artificial Intelligence (249 citations), Cancer Research (82 citations), Molecular Biology (322 citations), Signal Processing (43 citations) and Pulmonary and Respiratory Medicine (121 citations). James Lowey has collaborated with scholars based in United States, Sweden and Canada. Frequent co-authors include Edward Suh, Zhe Xiong, E.R. Dougherty, Hua Jiang, Edward R. Dougherty, Jianping Hua, Chao Sima, Marcel Brun, B.R. Carroll and Waibhav Tembe. Their work appears in journals such as Bioinformatics, Pattern Recognition, Clinical Pharmacology & Therapeutics, Canadian Journal of Zoology and Genome Research.

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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