Tane S. Ray

30 papers receiving 2.2k citations

Tane S. Ray's Hit Papers

Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning 2002 · 1.9k citations
1.9k0+8+16Years since publication50010001.5k

Peers

Tane S. Ray
Comparison fields: 5 of 142
  • Pathology and Forensic Medicine 543
  • Genetics 230
  • Molecular Biology 1.0k
  • Condensed Matter Physics 174
  • Oncology 299
Replace Jaegil Kim with:
Jaegil Kim United States
Taku A. Tokuyasu United States
A. Krasnitz United States
Joshua J. Waterfall United States
Orly Alter United States
Alberto Gandolfi Italy
Adriano Barra Italy
Tadashi Kadowaki Japan
Dirk Drasdo Germany
Sabine Mai Canada
Tane S. Ray relative to Jaegil Kim United States Jaegil Kim's profile →
Citations per field
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Citations per year

Countries citing papers authored by Tane S. Ray

Since Specialization
Citations

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

Fields of papers citing papers by Tane S. Ray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tane S. Ray. 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 Tane S. Ray. The network helps show where Tane S. Ray may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning
Hit paper breakdown →
20021855
2 199447
3 200844
4 201736
5 198934
6 198928
7 199020
8 198219
9 199018
10 198817
11 199217
12 199911
13 199811
14 19949
15 19839
16 19939
17 19918
18 19948
19 19997
20 19926

About Tane S. Ray

Tane S. Ray is a scholar working on Condensed Matter Physics, Mathematical Physics, Molecular Biology, Statistical and Nonlinear Physics and Atomic and Molecular Physics, and Optics, having authored 31 papers that have together received 2.2k indexed citations. Recurring topics across this work include Theoretical and Computational Physics (16 papers), Stochastic processes and statistical mechanics (9 papers), Evolution and Genetic Dynamics (4 papers), Complex Systems and Time Series Analysis (4 papers), Random lasers and scattering media (3 papers), Evolutionary Game Theory and Cooperation (3 papers), Material Dynamics and Properties (3 papers) and Quantum many-body systems (2 papers). The work is most often cited by research in Pathology and Forensic Medicine (543 citations), Genetics (230 citations), Molecular Biology (1.0k citations), Condensed Matter Physics (174 citations) and Oncology (299 citations). Tane S. Ray has collaborated with scholars based in United States, Canada and Barbados. Frequent co-authors include Pablo Tamayo, Ricardo C.T. Aguiar, T. Andrew Lister, Kim Last, Ken N. Ross, Andrew P. Weng, Todd R. Golub, Geraldine S. Pinkus, Jeffery L. Kutok and Michelle Gaasenbeek. Their work appears in journals such as Journal of Statistical Physics, Physical Review A, International Journal of Modern Physics C, Physical Review Letters and Theory in Biosciences.

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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