Richard E. Neapolitan

2.9k total citations
56 papers, 1.5k citations indexed

About

Richard E. Neapolitan is a scholar working on Artificial Intelligence, Molecular Biology and Management Science and Operations Research. According to data from OpenAlex, Richard E. Neapolitan has authored 56 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 16 papers in Molecular Biology and 9 papers in Management Science and Operations Research. Recurrent topics in Richard E. Neapolitan's work include Bayesian Modeling and Causal Inference (18 papers), Bioinformatics and Genomic Networks (11 papers) and AI-based Problem Solving and Planning (9 papers). Richard E. Neapolitan is often cited by papers focused on Bayesian Modeling and Causal Inference (18 papers), Bioinformatics and Genomic Networks (11 papers) and AI-based Problem Solving and Planning (9 papers). Richard E. Neapolitan collaborates with scholars based in United States and South Korea. Richard E. Neapolitan's co-authors include Peter M. Jones, Xia Jiang, Shyam Visweswaran, M. Michael Barmada, Xia Jiang, Zexian Zeng, Scott B. Morris, Adam Brufsky, Alan Wells and Curt M. Horvath and has published in prestigious journals such as PLoS ONE, Technometrics and BMC Bioinformatics.

In The Last Decade

Richard E. Neapolitan

53 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Richard E. Neapolitan United States 18 766 272 175 141 134 56 1.5k
Anthony Brabazon Ireland 22 994 1.3× 149 0.5× 237 1.4× 54 0.4× 59 0.4× 120 1.5k
Sameem Abdul Kareem Malaysia 23 614 0.8× 120 0.4× 131 0.7× 71 0.5× 126 0.9× 104 1.7k
Jun Sakuma Japan 20 772 1.0× 139 0.5× 86 0.5× 170 1.2× 213 1.6× 120 1.3k
Irad Ben‐Gal Israel 17 289 0.4× 228 0.8× 165 0.9× 150 1.1× 85 0.6× 105 1.3k
Stig Kjær Andersen Denmark 13 782 1.0× 151 0.6× 205 1.2× 84 0.6× 103 0.8× 53 1.4k
Noelia Sánchez‐Maroño Spain 17 1.5k 1.9× 824 3.0× 85 0.5× 233 1.7× 247 1.8× 55 2.6k
Yun Xiong China 21 779 1.0× 245 0.9× 119 0.7× 119 0.8× 420 3.1× 116 1.4k
Mourad Oussalah Finland 28 1.1k 1.4× 138 0.5× 197 1.1× 213 1.5× 309 2.3× 203 2.3k
Pedro Henriques Abreu Portugal 20 895 1.2× 131 0.5× 54 0.3× 146 1.0× 138 1.0× 82 1.7k
Michael Cochez Germany 16 881 1.2× 277 1.0× 165 0.9× 117 0.8× 228 1.7× 46 1.6k

Countries citing papers authored by Richard E. Neapolitan

Since Specialization
Citations

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

Fields of papers citing papers by Richard E. Neapolitan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard E. Neapolitan. 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 Richard E. Neapolitan. The network helps show where Richard E. Neapolitan may publish in the future.

Co-authorship network of co-authors of Richard E. Neapolitan

This figure shows the co-authorship network connecting the top 25 collaborators of Richard E. Neapolitan. A scholar is included among the top collaborators of Richard E. Neapolitan 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 Richard E. Neapolitan. Richard E. Neapolitan 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.
Jiang, Xia, Alan Wells, Adam Brufsky, & Richard E. Neapolitan. (2019). A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis. PLoS ONE. 14(3). e0213292–e0213292. 32 indexed citations
2.
Zeng, Zexian, Xiaoyu Li, Seema A. Khan, et al.. (2018). Using natural language processing and machine learning to identify breast cancer local recurrence. BMC Bioinformatics. 19(S17). 498–498. 56 indexed citations
3.
Zeng, Zexian, Xia Jiang, Xiaoyu Li, et al.. (2018). Conjugated equine estrogen and medroxyprogesterone acetate are associated with decreased risk of breast cancer relative to bioidentical hormone therapy and controls. PLoS ONE. 13(5). e0197064–e0197064. 8 indexed citations
4.
Neapolitan, Richard E. & Xia Jiang. (2017). The Bayesian Network Story. Oxford University Press eBooks. 1 indexed citations
5.
Neapolitan, Richard E., Xia Jiang, Daniela P. Ladner, & Bruce Kaplan. (2016). A Primer on Bayesian Decision Analysis With an Application to a Kidney Transplant Decision. Transplantation. 100(3). 489–496. 8 indexed citations
6.
Jiang, Xia, et al.. (2015). Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring. PLoS ONE. 10(12). e0143247–e0143247. 15 indexed citations
7.
Neapolitan, Richard E. & Xia Jiang. (2015). Study of Integrated Heterogeneous Data Reveals Prognostic Power of Gene Expression for Breast Cancer Survival. PLoS ONE. 10(2). e0117658–e0117658. 6 indexed citations
8.
Neapolitan, Richard E., Curt M. Horvath, & Xia Jiang. (2015). Pan-cancer analysis of TCGA data reveals notable signaling pathways. BMC Cancer. 15(1). 516–516. 28 indexed citations
9.
Jiang, Xia & Richard E. Neapolitan. (2015). Evaluation of a two-stage framework for prediction using big genomic data. Briefings in Bioinformatics. 16(6). 912–921. 6 indexed citations
10.
Jiang, Xia, et al.. (2014). A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets. Journal of the American Medical Informatics Association. 21(e2). e312–e319. 9 indexed citations
11.
Jiang, Xia & Richard E. Neapolitan. (2012). Mining Pure, Strict Epistatic Interactions from High-Dimensional Datasets: Ameliorating the Curse of Dimensionality. PLoS ONE. 7(10). e46771–e46771. 13 indexed citations
12.
Jiang, Xia, Richard E. Neapolitan, M. Michael Barmada, & Shyam Visweswaran. (2011). Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinformatics. 12(1). 89–89. 74 indexed citations
13.
Neapolitan, Richard E., et al.. (2009). Foundations of Algorithms, Fourth Edition. 3 indexed citations
14.
Neapolitan, Richard E., et al.. (2008). Foundations of Algorithms using C++ Pseudocode, Third Edition. 41(6). 617–617. 6 indexed citations
15.
Neapolitan, Richard E., et al.. (1997). Foundations of algorithms using C++ pseudocode (2nd ed.). 8 indexed citations
16.
Neapolitan, Richard E.. (1992). A survey of uncertain and approximate inference. John Wiley & Sons, Inc. eBooks. 55–82. 9 indexed citations
17.
Neapolitan, Richard E., et al.. (1992). A model theoretic approach to propositional fuzzy logic using Beth Tableaux. John Wiley & Sons, Inc. eBooks. 141–157. 7 indexed citations
18.
Neapolitan, Richard E.. (1991). The principle of interval constraints: A generalization of the symmetric dirichlet distribution. Mathematical Biosciences. 103(1). 33–44. 1 indexed citations
19.
Neapolitan, Richard E., et al.. (1987). Using Set Covering and Uncertain Reasoning to Determine Treatments. PubMed Central. 213–219. 3 indexed citations
20.
Neapolitan, Richard E.. (1987). MODELS FOR REASONING UNDER UNCERTAINTY. Applied Artificial Intelligence. 1(4). 337–366. 9 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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