Edward R. Dougherty

Molecular Biology · 2000–2014

A researcher in the Science corpus: 14 papers; trusts 14 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 60 citations into it; #993 of 17,773 by trust here, #3,389 by citations
• Artificial Intelligence — 18 citations into it; #6,300 of 35,345 by trust here, #15,236 by citations
• Statistics and Probability — 12 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Statistical Learning Theory — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Edward R. Dougherty trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Bradley Efron
2. Vladimir Naumovich Vapnik
3. Ilya Shmulevich
4. Luc Devroye
5. Tom Fawcett
6. Nir Friedman
7. David E. Goldberg
8. László Györfi
9. M. J. van de Vijver
10. Alexander J. Hartemink

Who trusts Edward R. Dougherty overall

1. Ilya Shmulevich
2. Chakkrit Kla Tantithamthavorn
3. Ji-Hyun Kim
4. DaiZhan Cheng
5. A. Y. Mitrophanov
6. Hongsheng Qi
7. Richard Jensen
8. Edwin Raczko
9. Yvan Saeys
10. Shane McIntosh

Papers from Edward R. Dougherty's perspective

Ordered by how strongly the researchers Edward R. Dougherty trusts endorse each paper. Same corpus, a different order for every researcher.

1. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling (Nature)
2. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. (Science)
3. Using Bayesian Networks to Analyze Expression Data (Annual International Conference on Research in Computational Molecular Biology)
4. Statistical learning theory
5. Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer (Nature Biotechnology)
6. The Nature of Statistical Learning Theory, Second Edition (Statistics for Engineering and Information Science)
7. A theory of the learnable
8. Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks (Pacific Symposium on Biocomputing)
9. Multifaceted physiological response allows yeast to adapt to the loss of the signal recognition particle-dependent protein-targeting pathway. (Molecular Biology of the Cell)
10. Genetic Algorithms in Search Optimization and Machine Learning

Their papers

• Gene selection: a Bayesian variable selection approach (2003) — 390 citations
• Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks (2002) — 1,680 citations
• Molecular classification of cutaneous malignant melanoma by gene expression profiling (2000) — 2,061 citations
• Is cross-validation valid for small-sample microarray classification? (2004) — 638 citations
• Gene perturbation and intervention in probabilistic Boolean networks (2002) — 308 citations
• Inferring gene regulatory networks from time series data using the minimum description length principle (2006) — 375 citations
• Optimal number of features as a function of sample size for various classification rules (2005) — 451 citations
• Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network (2010) — 69 citations
• Effect of separate sampling on classification accuracy (2014)
• Optimal robust classifiers (2005) — 14 citations
• Relation Between Permutation-Test P Values and Classifier Error Estimates (2003) — 25 citations
• Hands-on Morphological Image Processing (2003) — 593 citations
• From Boolean to probabilistic Boolean networks as models of genetic regulatory networks (2002) — 636 citations
• Gene-expression profiles in hereditary breast cancer. (2001) — 969 citations