Mark A. DePristo
Molecular Biology · 2005–2019
A researcher in the Science corpus: 26 papers; trusts 19 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 301 citations into it; #2,969 of 17,773 by trust here, #158 by citations
- Artificial Intelligence — 87 citations into it
- Computer Vision and Pattern Recognition — 40 citations into it; #21,414 of 21,575 by trust here, #9,435 by citations
- Ecology — 18 citations into it
- Model Architectures — 17 citations into it
- Safety, Robustness & Interpretability — 11 citations into it; #2,233 of 2,251 by trust here, #1,205 by citations
- Cell Biology — 8 citations into it; #634 of 2,073 by trust here, #803 by citations
Whom Mark A. DePristo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Heng Li
- A. Goldberg
- Christopher M. Dobson
- Geoffrey E. Hinton
- A. Fink
- Ben Langmead
- Richard Durbin
- Diederik P. Kingma
- Kaiming He
- Sepp Hochreiter
Who trusts Mark A. DePristo overall
- Heng Li
- Nobuhiko Tokuriki
- S. Bershtein
- Konstantin B. Zeldovich
- B. Feng
- Jakub Otwinowski
- Bian Li
- Raquel Dias
- Eugene I. Shakhnovich
- B. Knaus
Papers from Mark A. DePristo's perspective
Ordered by how strongly the researchers Mark A. DePristo trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- ImageNet Large Scale Visual Recognition Challenge
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Dropout: a simple way to prevent neural networks from overfitting
- Attention is All you Need (Neural Information Processing Systems)
- Distilling the Knowledge in a Neural Network (arXiv.org)
Their papers
- Likelihood Ratios for Out-of-Distribution Detection (2019) — 840 citations
- A guide to deep learning in healthcare (2019) — 4,405 citations
- A framework for variation discovery and genotyping using next-generation DNA sequencing data (2011) — 11,249 citations
- The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. (2010) — 25,882 citations
- A global reference for human genetic variation (2015) — 16,925 citations
- Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome (2019) — 1,512 citations
- Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins (2006) — 1,383 citations
- A systematic survey of loss-of-function variants in human protein-coding genes (2012) — 1,227 citations
- Knowledge-based real-space explorations for low-resolution structure determination. (2006) — 28 citations
- A framework for the interpretation of de novo mutation in human disease (2014) — 1,035 citations
- From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline (2013) — 6,636 citations
- Simultaneous determination of protein structure and dynamics (2005) — 683 citations
- Missense meanderings in sequence space: a biophysical view of protein evolution (2005) — 646 citations
- A polygenic burden of rare disruptive mutations in schizophrenia (2014) — 1,469 citations
- Creating a universal SNP and small indel variant caller with deep neural networks (2016) — 103 citations
- Using deep learning to annotate the protein universe (2019) — 304 citations
- Sub-lethal antibiotic treatment leads to multidrug resistance via radical-induced mutagenesis (2010) — 959 citations
- The genetic architecture of type 2 diabetes (2016) — 1,066 citations
- Scaling accurate genetic variant discovery to tens of thousands of samples (2017) — 1,758 citations
- A deep learning approach to pattern recognition for short DNA sequences (2018) — 59 citations