James D. Malley

108 papers receiving 7.3k citations

Hit Papers

An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. 2009 · 1.9k citations
1.9k200920262014202050010001.5k

Peers

James D. Malley
Comparison fields: 5 of 224
  • Genetics 1.1k
  • Statistics and Probability 276
  • Rheumatology 484
  • Immunology 586
  • Artificial Intelligence 848
Replace Frédérique Lisacek with:
Frédérique Lisacek Switzerland
Natacha Turck Switzerland
Martin Krzywinski Canada
Alexandre Hainard Switzerland
Markus Müller Switzerland
Xavier Robin Switzerland
William Rand United States
Paul R. Burton Australia
Natalia Tiberti Italy
Jean-Charles Sanchez Switzerland
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Citations per field
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Citations per year

Countries citing papers authored by James D. Malley

Since Specialization
Citations

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

Fields of papers citing papers by James D. Malley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 201648
2 20162
3 201511
4 20133
5 201152
6
An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests.
Hit paper breakdown →
20091865
7 200920
8 200826
9 200796
10 200657
11 2003428
12 2002349
13 200212
14 200275
15 200047
16 2000157
17 199849
18 199458
19 19946
20 198210

About James D. Malley

James D. Malley is a scholar working on Behavioral Neuroscience, Statistics and Probability, Gastroenterology, Genetics and Artificial Intelligence, having authored 110 papers that have together received 7.5k indexed citations. Recurring topics across this work include Colorectal Cancer Screening and Detection (14 papers), Genetic Associations and Epidemiology (13 papers), Gene expression and cancer classification (12 papers), Quantum Mechanics and Applications (9 papers), Inflammatory Myopathies and Dermatomyositis (9 papers), Quantum Information and Cryptography (9 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Bioinformatics and Genomic Networks (7 papers). The work is most often cited by research in Genetics (1.1k citations), Statistics and Probability (276 citations), Rheumatology (484 citations), Immunology (586 citations) and Artificial Intelligence (848 citations). James D. Malley has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Carolin Strobl, Gerhard Tutz, Ronald M. Summers, Andreas Ziegler, Daniel Q. Naiman, Karen G. Malley, Michael Parisi, Scott Eastman, Rachel Nuttall and Brian Oliver. Their work appears in journals such as BioData Mining, Radiology, Journal of the American Statistical Association, Physics Letters A and Physical Review A.

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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