John M. Jumper | Academic Influence

John M. Jumper's AcademicInfluence.com Rankings

John M. Jumper

Computer Science

#6873

World Rank

#7239

Historical Rank

Database

#9096

World Rank

#9565

Historical Rank

computer-science Degrees

John M. Jumper

Biology

#15205

World Rank

#19136

Historical Rank

Bioinformatics

#207

World Rank

#209

Historical Rank

Computational Biology

#377

World Rank

#379

Historical Rank

biology Degrees

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Computer Science
Biology

John M. Jumper's Degrees

PhD Computational Biology Stanford University
Masters Bioinformatics University of California, Berkeley
Bachelors Biology University of California, Berkeley

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Why Is John M. Jumper Influential?

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According to Wikipedia, John Michael Jumper is an American senior research scientist at DeepMind Technologies. Jumper and his colleagues created AlphaFold, an artificial intelligence model to predict protein structures from their amino acid sequence with high accuracy. Jumper has stated that the AlphaFold team plans to release 100 million protein structures. The scientific journal Nature included Jumper as one of the ten "people who mattered" in science in their annual listing of Nature's 10 in 2021.

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John M. Jumper's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Highly accurate protein structure prediction with AlphaFold (2021) (8720)
Improved protein structure prediction using potentials from deep learning (2020) (1753)
Atomic-Level Characterization of the Structural Dynamics of Proteins (2010) (1528)
Highly accurate protein structure prediction for the human proteome (2021) (1032)
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2021) (831)
Protein complex prediction with AlphaFold-Multimer (2021) (682)
Oncogenic Mutations Counteract Intrinsic Disorder in the EGFR Kinase and Promote Receptor Dimerization (2012) (302)
Effective gene expression prediction from sequence by integrating long-range interactions (2021) (200)
Innovative scattering analysis shows that hydrophobic disordered proteins are expanded in water (2017) (142)
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) (2019) (137)
Applying and improving AlphaFold at CASP14 (2021) (111)
Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations (2014) (97)
Protein structure prediction using multiple deep neural networks in CASP13. (2019) (66)
Protein structure predictions to atomic accuracy with AlphaFold (2022) (62)
A 7 D De novo structure prediction with deep learning based scoring (53)
Free-Standing Kinked Silicon Nanowires for Probing Inter- and Intracellular Force Dynamics. (2015) (43)
Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs (2021) (33)
Response to Comment on “Innovative scattering analysis shows that hydrophobic disordered proteins are expanded in water” (2018) (29)
Improved protein structure prediction using potentials from deep learning (2020) (27)
Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours (2018) (23)
Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics (2018) (20)
Rotational relaxation in ortho-terphenyl: using atomistic simulations to bridge theory and experiment. (2013) (13)
Structure of the decoy module of human glycoprotein 2 and uromodulin and its interaction with bacterial adhesin FimH (2022) (8)
A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations. (2018) (6)
Discovery of archaeal fusexins homologous to eukaryotic HAP2/GCS1 gamete fusion proteins (2022) (5)
On the Interpretation of Force-Induced Unfolding Studies of Membrane Proteins Using Fast Simulations. (2019) (5)
Accelerating Large Language Model Decoding with Speculative Sampling (2023) (4)
Maximum-likelihood, self-consistent side chain free energies with applications to protein molecular dynamics (2016) (3)
Archaeal origins of gamete fusion (2021) (3)
Trajectory-Based Parameterization of a Coarse-Grained Forcefield for High-Thoughput Protein Simulation (2017) (3)
Rapid calculation of side chain packing and free energy with applications to protein molecular dynamics (2016) (3)
3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources (2022) (2)
Structural basis of template strand deoxyuridine promoter recognition by a viral RNA polymerase (2021) (2)
Structure of the PAPP-ABP5 complex reveals mechanism of substrate recognition (2022) (2)
Including H-Bonding in Depth-Dependent Membrane Burial Potentials for Improving Folding Simulations (2016) (1)
Upside: A New Dynamics Method Capable of Cooperative De Novo Protein Folding in CPU-Hours (2016) (0)
Folding Membrane Proteins by Contacts Inferred from Non-Membrane Proteins and Near-Atomic Level Refinement (2017) (0)
Extending Upside, a Near-Atomic Level Model for Fast Protein Folding, for Predicting Protein-Protein Interactions (2017) (0)
AlphaFold: Improved protein structure prediction using (2019) (0)
Characterizing Disordered Protein Ensembles Using Small‐angle Scattering (2018) (0)
Upside: Protein Folding in CPU-Hours with Applications to Force-Unfolding of Membrane Proteins (2020) (0)
Measuring the solvent quality of water for disordered proteins from a single SAXS measurement (2018) (0)
Molecular Determinants of Specificity in the Dpr-DIP Interaction Network (2017) (0)
Fast, Atomic-Level AFM and Magnetic Tweezers Simulations of the Unfolding of Membrane Proteins using a New Membrane Burial Potential with H-Bonding (2019) (0)
New Methods Using Rigorous Machine Learning for Coarse-Grained Protein Folding and Dynamics (2017) (0)
Measuring the (Good) Solvent Quality of Water for Disordered Proteins from a Single SAXS Measurement (2017) (0)
Author Correction: Structure of the PAPP-ABP5 complex reveals mechanism of substrate recognition (2022) (0)

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What Schools Are Affiliated With John M. Jumper?

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