B. G. Buchanan

1.6k total citations
33 papers, 982 citations indexed

About

B. G. Buchanan is a scholar working on Artificial Intelligence, Spectroscopy and Information Systems. According to data from OpenAlex, B. G. Buchanan has authored 33 papers receiving a total of 982 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 8 papers in Spectroscopy and 5 papers in Information Systems. Recurrent topics in B. G. Buchanan's work include AI-based Problem Solving and Planning (9 papers), Analytical Chemistry and Chromatography (6 papers) and Data Mining Algorithms and Applications (5 papers). B. G. Buchanan is often cited by papers focused on AI-based Problem Solving and Planning (9 papers), Analytical Chemistry and Chromatography (6 papers) and Data Mining Algorithms and Applications (5 papers). B. G. Buchanan collaborates with scholars based in United States, Australia and Canada. B. G. Buchanan's co-authors include Edward A. Feigenbaum, Edward H. Shortliffe, Reid G. Smith, Joshua Lederberg, Diana E. Forsythe, Gregory F. Cooper, Carl Djerassi, Paul Hanbury, Wendy W. Chapman and Will Bridewell and has published in prestigious journals such as Journal of the American Chemical Society, Analytical Chemistry and Proceedings of the IEEE.

In The Last Decade

B. G. Buchanan

31 papers receiving 821 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
B. G. Buchanan United States 16 420 242 128 124 99 33 982
Gunther Schadow United States 18 392 0.9× 397 1.6× 60 0.5× 382 3.1× 15 0.2× 42 1.1k
Qing Zeng United States 28 866 2.1× 1.2k 5.0× 140 1.1× 618 5.0× 35 0.4× 96 2.5k
Travis B. Murdoch Canada 10 300 0.7× 312 1.3× 10 0.1× 277 2.2× 14 0.1× 11 1.5k
Jan Talmon Netherlands 22 165 0.4× 171 0.7× 13 0.1× 697 5.6× 23 0.2× 96 1.7k
Xia Ning United States 17 726 1.7× 260 1.1× 31 0.2× 38 0.3× 225 2.3× 101 1.7k
Mathias Brochhausen United States 16 336 0.8× 462 1.9× 11 0.1× 107 0.9× 77 0.8× 76 710
Páll Jónsson United Kingdom 15 167 0.4× 436 1.8× 15 0.1× 82 0.7× 134 1.4× 41 1.4k
Matthias Samwald Austria 22 759 1.8× 660 2.7× 6 0.0× 113 0.9× 145 1.5× 70 1.5k
Erik M. van Mulligen Netherlands 29 986 2.3× 1.2k 5.0× 11 0.1× 166 1.3× 254 2.6× 107 1.9k
Lixia Yao United States 20 189 0.5× 405 1.7× 28 0.2× 68 0.5× 132 1.3× 95 1.2k

Countries citing papers authored by B. G. Buchanan

Since Specialization
Citations

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

Fields of papers citing papers by B. G. Buchanan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by B. G. Buchanan. 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 B. G. Buchanan. The network helps show where B. G. Buchanan may publish in the future.

Co-authorship network of co-authors of B. G. Buchanan

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

All Works

20 of 20 papers shown
1.
Buchanan, B. G. & Mark Collard. (2008). Testing models of early Paleoindian colonization and adaptation using cladistics. 6 indexed citations
2.
3.
Buchanan, B. G., et al.. (2002). What's new? Using prior models as a measure of novelty in knowledge discovery. 86–89. 3 indexed citations
4.
Chapman, Wendy W., Will Bridewell, Paul Hanbury, Gregory F. Cooper, & B. G. Buchanan. (2001). Evaluation of negation phrases in narrative clinical reports.. PubMed. 105–9. 127 indexed citations
5.
Ambrosino, Richard & B. G. Buchanan. (1999). The use of physician domain knowledge to improve the learning of rule-based models for decision-support.. PubMed. 192–6. 9 indexed citations
6.
Buchanan, B. G., et al.. (1996). Carcinogenicity predictions for a group of 30 chemicals undergoing rodent cancer bioassays based on rules derived from subchronic organ toxicities.. Environmental Health Perspectives. 104(suppl 5). 1059–1063. 16 indexed citations
7.
Stead, William W., R. Brian Haynes, Sherrilynne S. Fuller, et al.. (1994). Designing Medical Informatics Research and Library--Resource Projects to Increase What Is Learned. Journal of the American Medical Informatics Association. 1(1). 28–33. 79 indexed citations
8.
Buchanan, B. G., et al.. (1992). Involving patients in health care: explanation in the clinical setting.. PubMed. 510–4. 15 indexed citations
9.
Forsythe, Diana E. & B. G. Buchanan. (1989). Knowledge acquisition for expert systems: some pitfalls and suggestions. IEEE Transactions on Systems Man and Cybernetics. 19(3). 435–442. 68 indexed citations
10.
Buchanan, B. G. & Reid G. Smith. (1988). Fundamentals of Expert Systems. 3(1). 23–58. 98 indexed citations
11.
Buchanan, B. G., et al.. (1988). Effective management strategy for establishing an operating room satellite pharmacy.. PubMed. 23(11). 961–8. 3 indexed citations
12.
Lindsay, Robert, Edward A. Feigenbaum, B. G. Buchanan, & Joshua Lederberg. (1980). Applications of Artificial Intelligence for Chemical Inference: The Dendral Project. McGraw-Hill, Inc. eBooks. 49 indexed citations
13.
Gray, N. A. B., Raymond E. Carhart, A. Lavanchy, et al.. (1980). Computerized mass spectrum prediction and ranking. Analytical Chemistry. 52(7). 1095–1102. 20 indexed citations
14.
Shortliffe, Edward H., B. G. Buchanan, & Edward A. Feigenbaum. (1979). Knowledge engineering for medical decision making: A review of computer-based clinical decision aids. Proceedings of the IEEE. 67(9). 1207–1224. 187 indexed citations
15.
Buchanan, B. G., et al.. (1978). A comparative study of five commercial reagents for the Coulter Model S: a proposed method for reagent evaluation.. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 102(5). 258–62. 1 indexed citations
16.
17.
Smith, Dennis H., B. G. Buchanan, Robert S. Engelmore, et al.. (1972). Applications of artificial intelligence for chemical inference. VIII. Approach to the computer interpretation of the high resolution mass spectra of complex molecules. Structure elucidation of estrogenic steroids. Journal of the American Chemical Society. 94(17). 5962–5971. 34 indexed citations
18.
Buchs, Armand, A. M. Duffield, Carl Djerassi, et al.. (1970). Applications of “Artificial Intelligence” for Chemical Inference, VI. Approach to a General Method of Interpreting Low Resolution Mass Spectra with a Computer. Helvetica Chimica Acta. 53(6). 1394–1417. 34 indexed citations
19.
Buchanan, B. G., Carl Djerassi, A. M. Duffield, et al.. (1969). Applications of artificial intelligence for chemical inference. I - The number of possible organic compounds - Acyclic structures containing C, H, O, and N.. NASA Technical Reports Server (NASA). 19 indexed citations
20.
Schroll, Gustav, A. M. Duffield, Carl Djerassi, et al.. (1969). Applications of artificial intelligence for chemical inference. III. Aliphatic ethers diagnosed by their low-resolution mass spectra and nuclear magnetic resonance data. Journal of the American Chemical Society. 91(26). 7440–7445. 38 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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