Marylyn DeRiggi Ritchie

Genetics · 2001–2021

A researcher in the Science corpus: 18 papers; trusts 8 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Genetics — 208 citations into it; #515 of 6,854 by trust here, #354 by citations
• Molecular Biology — 155 citations into it; #1,731 of 17,773 by trust here, #2,926 by citations
• Artificial Intelligence — 15 citations into it
• Statistics and Probability — 13 citations into it
• Information Systems — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Marylyn DeRiggi Ritchie trusts overall

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

1. David Ruppert
2. Erick Cantú-Paz
3. Lance W. Hahn
4. Jason H. Moore
5. Yann LeCun
6. Hong-Bin Shen
7. Alkes L Price
8. Hui Zou
9. Casey S. Greene
10. Matthew R. Nelson

Who trusts Marylyn DeRiggi Ritchie overall

1. Jason H. Moore
2. Kathryn L. Lunetta
3. Lance W. Hahn
4. Digna R. Velez
5. Zexian Zeng
6. J. Li
7. Bill C. White
8. L. Hayward
9. Yu Zhang
10. Sijia Huang

Papers from Marylyn DeRiggi Ritchie's perspective

Ordered by how strongly the researchers Marylyn DeRiggi Ritchie 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Methods of integrating data to uncover genotype–phenotype interactions (2015) — 983 citations
• A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction (2007) — 398 citations
• Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. (2001) — 1,894 citations
• PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations (2010) — 1,141 citations
• Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions (2003) — 1,147 citations
• Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data (2013) — 986 citations
• Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity (2003) — 568 citations
• The power of genetic diversity in genome-wide association studies of lipids (2021) — 765 citations
• Mapping the human genetic architecture of COVID-19 (2021) — 807 citations
• Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment (2018) — 2,165 citations
• Celebrating parasites (2017) — 37 citations
• Defining the role of common variation in the genomic and biological architecture of adult human height (2014)
• Genome- and Phenome-Wide Analysis of Cardiac Conduction Identifies Markers of Arrhythmia Risk (2013) — 177 citations
• Generalization and Dilution of Association Results from European GWAS in Populations of Non-European Ancestry: The PAGE Study (2013) — 238 citations
• Quality Control Procedures for Genome Wide Association Studies (2011) — 400 citations
• The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies (2011) — 709 citations
• Predicting Clopidogrel Response Using DNA Samples Linked to an Electronic Health Record (2011) — 103 citations
• Genetic determinants of response to warfarin during initial anticoagulation. (2008) — 604 citations