Richard M. Simon

Molecular Biology · 1985–2009

A researcher in the Science corpus: 15 papers; trusts 27 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 49 citations into it; #590 of 17,773 by trust here, #2,960 by citations
• Statistics and Probability — 9 citations into it; #5,453 of 12,932 by trust here, #6,552 by citations
• Artificial Intelligence — 7 citations into it; #7,055 of 35,345 by trust here, #16,471 by citations

Whom Richard M. Simon trusts overall

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

1. Robert Tibshirani
2. Bradley Efron
3. Vladimir Naumovich Vapnik
4. Edward L. Korn
5. Yoav Benjamini
6. Trevor J. Hastie
7. Yann LeCun
8. M. Stone
9. Eric Bair
10. Lisa M. McShane

Who trusts Richard M. Simon overall

1. Davis J. McCarthy
2. Joseph P. Romano
3. Baqun Zhang
4. Ji-Hyun Kim
5. Aki Vehtari
6. Gordon K. Smyth
7. Charlotte Soneson
8. Tal Yarkoni
9. Tyler J. VanderWeele
10. Eric B. Laber

Papers from Richard M. Simon's perspective

Ordered by how strongly the researchers Richard M. Simon trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A random variance model for detection of differential gene expression in small microarray experiments (2003) — 705 citations
• Bias in error estimation when using cross-validation for model selection (2006) — 2,007 citations
• The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. (2003) — 970 citations
• Controlling the number of false discoveries: application to high-dimensional genomic data (2004) — 296 citations
• Prediction error estimation: a comparison of resampling methods (2005) — 1,299 citations
• Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning (2003) — 845 citations
• A Paradigm for Class Prediction Using Gene Expression Profiles (2002) — 317 citations
• Relation of Gene Expression Phenotype to Immunoglobulin Mutation Genotype in B Cell Chronic Lymphocytic Leukemia (2001) — 1,219 citations
• Use of archived specimens in evaluation of prognostic and predictive biomarkers. (2009) — 1,041 citations
• Analysis of Gene Expression Data Using BRB-Array Tools (2007) — 676 citations
• Molecular diagnosis of Burkitt's lymphoma. (2006) — 1,004 citations
• Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. (2004) — 1,431 citations
• Molecular Diagnosis of Primary Mediastinal B Cell Lymphoma Identifies a Clinically Favorable Subgroup of Diffuse Large B Cell Lymphoma Related to Hodgkin Lymphoma (2003) — 1,127 citations
• The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. (2002) — 3,853 citations
• Testing for qualitative interactions between treatment effects and patient subsets. (1985)