John Ingraham

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Debora MarksSystems Biology, Harvard Medical School, Broad Institute of Harvard and MITVerificeret mail på hms.harvard.edu
Chris SanderHarvard Medical School, Broad Institute, MGH, Dana-Farber Cancer Institute, Memorial Sloan KetteringVerificeret mail på sanderlab.org
Adam RiesselmanHippo HarvestVerificeret mail på hippoharvest.com
Thomas A. HopfScientific ConsultantVerificeret mail på thomashopf.com
Michael Springerharvard medical schoolVerificeret mail på hms.harvard.edu
Charlotta ScharfePrincipal Data Scientist, F. Hoffmann-La Roche, SwitzerlandVerificeret mail på roche.com
Frank PoelwijkGenerate Biomedicines LLCVerificeret mail på generatebiomedicines.com
Agnes Toth-PetroczyMPI of Molecular Cell Biology and Genetics and Center for Systems Biology DresdenVerificeret mail på mpi-cbg.de
Perry PalmedoHarvard UniversityVerificeret mail på fas.harvard.edu
Vikas GargMassachusetts Institute of Technology (MIT)Verificeret mail på mit.edu
Tommi JaakkolaMITVerificeret mail på csail.mit.edu
Regina BarzilayProfessor of EECS, MITVerificeret mail på csail.mit.edu
Dieter ArmbrusterProfessor of Mathematics, Arizona State UniversityVerificeret mail på asu.edu
Benjamin SchubertGroup Leader, ICB@Helmholtz Center MunichVerificeret mail på helmholtz-muenchen.de
Anna G. GreenAssistant Professor, Manning College of Information and Computer Sciences, UMass, AmherstVerificeret mail på umass.edu
Christian DallagoNVIDIA & Technische Universität MünchenVerificeret mail på tum.de
Kelly Paige BrockNortheastern University, Institute for Experiential AIVerificeret mail på northeastern.edu
Jue WangUniversity of WashingtonVerificeret mail på uw.edu
Renan Escalante-ChongLoxo Oncology at Lilly

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John Ingraham

Generate Biomedicines, Inc

Verificeret mail på csail.mit.edu - Startside

Machine Learning Computational Biology Protein Design


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
Mutation effects predicted from sequence co-variation TA Hopf, JB Ingraham, FJ Poelwijk, CPI Schärfe, M Springer, C Sander, ... Nature Biotechnology 35 (2), 128-135, 2017	734	2017
Deep generative models of genetic variation capture the effects of mutations AJ Riesselman, JB Ingraham, DS Marks Nature Methods 15, 816-822, 2018	674	2018
Generative Models for Graph-Based Protein Design J Ingraham, VK Garg, R Barzilay, T Jaakkola Neural Information Processing Systems, 2019	582	2019
Illuminating protein space with a programmable generative model JB Ingraham, M Baranov, Z Costello, KW Barber, W Wang, A Ismail, ... Nature 623 (7989), 1070-1078, 2023	358	2023
Defining variant-resistant epitopes targeted by SARS-CoV-2 antibodies: A global consortium study KM Hastie, H Li, D Bedinger, SL Schendel, SM Dennison, K Li, ... Science 374 (6566), 472-478, 2021	309	2021
Basketball teams as strategic networks JH Fewell, D Armbruster, J Ingraham, A Petersen, JS Waters PloS one 7 (11), e47445, 2012	265	2012
The EVcouplings Python framework for coevolutionary sequence analysis TA Hopf, AG Green, B Schubert, S Mersmann, CPI Schärfe, JB Ingraham, ... Bioinformatics 35 (9), 1582-1584, 2019	249	2019
3D RNA and Functional Interactions from Evolutionary Couplings C Weinreb, AJ Riesselman, JB Ingraham, T Gross, C Sander, DS Marks Cell 165 (4), 963-975, 2016	198	2016
Learning Protein Structure with a Differentiable Simulator J Ingraham, A Riesselman, C Sander, D Marks International Conference on Learning Representations, 2019	170	2019
Structured States of Disordered Proteins from Genomic Sequences A Toth-Petroczy, P Palmedo, J Ingraham, TA Hopf, B Berger, C Sander, ... Cell 167 (1), 158-170. e12, 2016	151	2016
Galactose metabolic genes in yeast respond to a ratio of galactose and glucose R Escalante-Chong, Y Savir, SM Carroll, JB Ingraham, J Wang, CJ Marx, ... Proceedings of the National Academy of Sciences 112 (5), 1636-1641, 2015	146	2015
Cellbox: Interpretable machine learning for perturbation biology with application to the design of cancer combination therapy B Yuan, C Shen, A Luna, A Korkut, DS Marks, J Ingraham, C Sander Cell systems 12 (2), 128-140. e4, 2021	126	2021
Generating transition states of isomerization reactions with deep learning L Pattanaik, JB Ingraham, CA Grambow, WH Green Physical Chemistry Chemical Physics 22 (41), 23618-23626, 2020	63	2020
Variational Inference for Sparse and Undirected Models J Ingraham, D Marks International Conference on Machine Learning, 2017	45*	2017
Simultaneous enhancement of multiple functional properties using evolution-informed protein design B Fram, Y Su, I Truebridge, AJ Riesselman, JB Ingraham, A Passera, ... Nature Communications 15 (1), 5141, 2024	10	2024
Antigen Binding Molecules Targeting SARS-CoV-2 G Grigoryan, J Ingraham, CL Leung, RS Federman, RJ Green, V Xue, ... US Patent App. 18/151,088, 2023	3	2023
Antigen binding molecules targeting SARS-CoV-2 G Grigoryan, J Ingraham US Patent 11,987,616, 2024	2	2024
Antigen binding molecules targeting SARS-CoV-2 G Grigoryan, J Ingraham, CL Leung, RS Federman, RJ Green, AH Ramos, ... US Patent 11,981,725, 2024	2	2024
Probabilistic Models of Structure in Biological Sequences JB Ingraham PQDT-Global, 2018	2	2018
Antigen binding molecules targeting sars-cov-2 G GRIGORYAN, J Ingraham, CL Leung, RS FEDERMAN, RJ GREEN, ...		2022

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