A.I. in Macromolecular Crystallography

This overview lists scientific publications concerned with machine learning in macromolecular crystallography. It's potentially incomplete; please contact us to add more!


First AuthorYearDOIPaper TitleTopicMethod
Lata1995https://doi.org/10.1016/0304-3991(95)00002-iAutomatic particle picking from electron micrographscryo-EM particle pickingclassification
Berntson2003https://doi.org/10.1107/S0909049503020855Application of a neural network in high-throughput protein crystallographydiffraction quality estimationclassification via cascade correlation NNs
Gopalakrishnan2004https://doi.org/10.1107/S090744490401683XMachine-learning techniques for macromolecular crystallization datacrystallization optimizationheuristics system, HAMB
Morris2004https://doi.org/10.1107/S090744490402061XStatistical pattern recognition for macromolecular crystallographersreviewpattern recognition
Pakric2004https://doi.org/10.1107/S0909049500012929Integration of macromolecular diffraction data using radial basis function networksX-ray intensity calculationregression via RBF NNs
Rupp2004https://doi.org/10.1016/j.ymeth.2004.03.031Predictive models for protein crystallizationcrystallization optimizationANN, GA
Wong2004https://doi.org/10.1016/j.jsb.2003.05.001Model-based particle picking for cryo-electron microscopycryo-EM particle picking
Liu2008https://doi.org/10.1107/S090744490802982XImage-based crystal detection: a machine-learning approachCrystal image recognitionboosting
Arbelaez2011https://doi.org/10.1016/j.jsb.2011.05.017Experimental evaluation of support vector machine-based and correlation-based approaches to automatic particle selectioncryo-EM particle pickingSVM
Ma2011https://doi.org/10.1109/TCBB.2011.52.A Novel α-Helix Identification Approach for Intermediate Resolution Electron Density Mapscryo-EM secondary structure recognitionclassification via boosting
Si2012https://doi.org/10.1002/bip.22063A Machine Learning Approach for the Identification of Protein Secondary Structure Elements from Electron Cryo-Microscopy Density Mapscryo-EM secondary structure recognitionclassification SVM
Ng2014https://doi.org/10.1107/S1399004714017581Using textons to rank crystallization droplets by the likely presence of crystalscrystal image recognitionis it AI?
Morshed2015https://doi.org/10.1107/S1399004715004241Using support vector machines to improve elemental ion identification in macromolecular crystal structurescrystallography model building & validationclassification via SVM
Touw2015https://doi.org/10.1107/S1399004715008263Detection of trans-cis flips and peptide-plane flips in protein structurescrystallography model building & validationRandom Forest
Cao2016https://doi.org/10.1186/s12859-016-1405-yDeepQA: improving the estimation of single protein model quality with deep belief networks
Li2016https://doi.org/10.1109/BIBM.2016.7822490Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopycryo-EM secondary structure recognitionclassification via CNN
Sharma2016https://doi.org/10.1107/S2053273316018696Asymmetry in serial femtosecond crystallography dataserial crystallography peak analysisis it AI?
Wang2016https://doi.org/10.1016/j.jsb.2016.07.006DeepPicker: A deep learning approach for fully automated particle picking in cryo-EMcryo-EM particle pickingCNN
Uziela2017https://doi.org/10.1093/bioinformatics/btw819ProQ3D: improved model quality assessments using deep learning
Xiao2017https://doi.org/10.1063/1.4982020A Fast Method for Particle Picking in Cryo-Electron Micrographs based on Fast R-CNNcryo-EM particle pickingclassification and object localization via CNN
Xu2017https://doi.org/10.1093/bioinformatics/btx230Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomogramscryo-EM tomography reconstructionclassification via CNN
Zhu2017https://doi.org/10.1186/s12859-017-1757-yA deep convolutional neural network approach to single-particle recognition in cryo-electron microscopycryo-EM particle pickingclassification via CNN
Bruno2018https://doi.org/10.1371/journal.pone.0198883Classification of crystallization outcomes using deep convolutional neural networkscrystal image recognitionclassification via CNN
Elbasir2018https://doi.org/10.1093/bioinformatics/bty953DeepCrystal: a deep learning framework for sequence-based protein crystallization predictionsequence-based protein crystallization predictionclassification via CNN
Heimowitz2018https://doi.org/10.1016/j.jsb.2018.08.012APPLE picker: Automatic particle picking, a low-effort cryo-EM frameworkcryo-EM particle pickingSVM
Ke2018https://doi.org/10.1107/S1600577518004873A convolutional neural network-based screening tool for X-ray serial crystallographyspot finding in serial crystallographyclassification via CNN
Kowiel2018https://doi.org/10.1093/bioinformatics/bty626Automatic recognition of ligands in electron density by machine learningligand detection in electron densityk-nearest neighbors, random forest, gradient boosting machine
Rozanov2018n/aAAnchor: CNN guided detection of anchor amino acids in high resolution cryo-EM density mapsautomatic model building in cryo-EMclassification via CNN
Sanchez-Garcia2018https://doi.org/10.1107/S2052252518014392Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopycryo-EM particle eliminationclassification via CNN
Thomas2018n/aTensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudsmethodTFN
Zeng2018https://doi.org/10.1016/j.jsb.2017.12.015A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentationcryo-EM tomography reconstructionconvolutional autoencoder
Aguiar2019https://doi.org/10.1126/sciadv.aaw1949Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learningspace group assigment in small molecule EDclassification via CNN
Al-Azzawi2019https://doi.org/10.1186/s12859-019-2926-yAutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM imagescryo-EM particle pickingclustering
Alnabati2019https://doi.org/10.3390/molecules25010082Advances in Structure Modeling Methods for Cryo-Electron Microscopy Mapsreviewvarious ML methods
Avramov2019https://doi.org/10.3390/molecules24061181Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Mapsresolution evaluation in CryoEMclassification via CNN
Bepler2019https://doi.org/10.1038/s41592-019-0575-8Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographscryo-EM particle pickingclassification via CNN
Chojnowski2019https://doi.org/10.1107/S2059798319009392Sequence assignment for low-resolution modelling of protein crystal structuressequence alignment in modellingclassification via SVM
Conover2019https://doi.org/10.1515/cmb-2019-0001AngularQA: protein model quality assessment with LSTM networks
Gagner2019https://doi.org/10.1038/s41598-019-55777-5Clustering of atomic displacement parameters in bovine trypsin reveals a distributed lattice of atoms with shared chemical propertiesADP similarityClustering
Garcia-Bonete2019https://doi.org/10.1107/S2053273319011446 Bayesian machine learning improves single-wavelength anomalous diffraction phasinganomalous phasing in MXBayesian machine learning model
Ito2019https://doi.org/10.1107/S160057751900434XDeepCentering: fully automated crystal centering using deep learning for macromolecular crystallographycrystal centeringobject localization via CNN
Ramirez-Aportela2019https://doi.org/10.1107/S2052252519011692DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy mapsresolution evaluation in CryoEMregression via CNN
Schurmann2019https://doi.org/10.1109/IEEECONF44664.2019.9048793Crystal centering using deep learning in X-ray crystallographycrystal centeringobject localization via CNN
Subramaniya2019https://doi.org/10.1038/s41592-019-0500-1Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learningcryo-EM secondary structure recognitionclassification via CNN
Sullivan2019https://doi.org/10.1107/S1600576719008665BraggNet: integrating Bragg peaks using neural networksneutron diffraction improvementclassification via CNN
Tegunov2019https://doi.org/10.1038/s41592-019-0580-y Real-time cryo-electron microscopy data preprocessing with Warpmicrograph preprocessingCNN
Vollmar2019https://doi.org/10.1107/S2052252520000895The predictive power of data-processing statisticsMX data qualityclassification via random forest and SVM
Wagner2019https://doi.org/10.1038/s42003-019-0437-zSPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EMcryo-EM particle pickingclassification and object localization via CNN
Xu2019n/aA2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes.amino acid recognition in modelling cryo-EM mapsCNN classification, then MCTS
Yao2019https://doi.org/10.1093/bioinformatics/btz728Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules.cryo-EM particle pickingclassification via FCN
Zhang2019https://doi.org/10.1186/s12859-019-2614-yPIXER: an automated particle-selection method based on segmentation using a deep neural networkcryo-EM particle pickingAE
Bepler2020https://doi.org/10.1038/s41467-020-18952-1Topaz-Denoise: general deep denoising models for cryoEM and cryoETmicrograph preprocessingCNN
Dalton2020https://doi.org/10.1101/2021.01.05.425510 Careless: A Variational Bayesian Model for Merging X-ray Diffraction Datadiffraction data processingBayesian machine learning model
Kappel2020https://doi.org/10.1038/s41592-020-0878-9Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures
Li2020https://doi.org/10.1109/BIBE50027.2020.00028Sequence-guided protein structure determination using graph convolutional and recurrent networks.GCN
Mostosi2020https://doi.org/10.1002/anie.202000421Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Mapscryo-EM secondary structure recognitionclassification via CNN
Sanchez-Garcia2020https://doi.org/10.1016/j.jsb.2020.107498MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learningcryo-EM particle eliminationclassification via CNN
Smith2020https://doi.org/10.1504/IJCBDD.2020.105095TopQA: a topological representation for single-model protein quality assessment with machine learning
van Schayck2020https://doi.org/10.1016/j.ultramic.2020.113091Sub-pixel electron detection using a convolutional neural networkdetector image processing in EMCNN
Chojnowski2021https://doi.org/10.1101/2021.04.18.440303Identification of unknown proteins in X-ray crystallography and cryo-EM model building in cryo-EM and crystallographyCNN
Czyzewski2021https://doi.org/10.1016/j.eswa.2021.114740Detecting anomalies in X-ray diffraction images using convolutional neural networksdiffraction image analysisCNN
Gagner2021https://doi.org/10.1088/2632-2153/ac022dEstimating the probability of coincidental similarity between atomic displacement parameters with machine learningADP similarityBayesian machine learning model
George2021https://doi.org/10.1038/s42003-021-01721-1CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopycryo-EM particle pickingsemantic segmentation, CNN
He2021https://doi.org/10.1093/bib/bbab156EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM mapscryo-EM secondary structure recognitionclassification via CNN
Kimanius2021https://doi.org/10.1107/S2052252520014384 Exploiting prior knowledge about biological macromolecules in cryo-EM structure determinationcryo-EM map reconstructionAE
Matsumoto2021https://doi.org/10.1038/s42256-020-00290-yExtraction of protein dynamics information from cryo-EM maps using deep learningcryo-EM dynamics analysisprediction of the logarithmic RMSF value via CNN
Miyaguchi2021https://doi.org/10.21203/rs.3.rs-687363/v1QAEmap: A Novel Local Quality Assessment Method for Protein Crystal Structures Using Machine Learningcrystallography model building & validation??? via CNN
Nguyen2021https://doi.org/10.1186/s12859-020-03948-xDRPnet: automated particle picking in cryo‑electron micrographs using deep regressioncryo-EM particle pickingobject localization and classification via CNN
Pfab2021https://doi.org/10.1073/pnas.2017525118DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexesautomatic model building in cryo-EMclassification via CNN
Sanchez-Garcia2021https://doi.org/10.1038/s42003-021-02399-1DeepEMhancer: a deep learning solution for cryo-EM volume post-processingcryo-EM map improvementdata processing via CNN
Si2021https://doi.org/10.1002/wcms.1542Artificial intelligence advances for de novo molecular structure modeling in cryo-electron microscopyreviewAI
Tegunov2021https://doi.org/10.1038/s41592-020-01054-7Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.5 Å in cellsreference-free motion correction and CTF estimationCNN and other
Zhong2021https://doi.org/10.1038/s41592-020-01049-4CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networkscryo-EM dynamics analysisVAE
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