Fetching the paper…
Reading the bibliography…
Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions.
Conditioning by adaptive sampling for robust design (2019)
Brookes, D. H., Park, H. & Listgarten, J · 1901
Earlier work this paper cites.
Learning protein sequence embeddings using information from structure (2019)
Bepler, T. & Berger, B · 1902
Earlier work this paper cites.
Exact gaussian processes on a million data points (2019)
Wang, K. A. et al · 1903
Earlier work this paper cites.
How to hallucinate functional proteins (2019)
Costello, Z. & Garcia Martin, H · 1903
Earlier work this paper cites.
On estimating regression
Nadaraya, E · 1964
Earlier work this paper cites.
Natural selection and the concept of a protein space
Smith, J. M · 1970
Earlier work this paper cites.
Amino acid substitution matrices from protein blocks
Henikoff, S. & Henikoff, J. G · 1992
Earlier work this paper cites.
Simpls: An alternative approach to partial least squares regression
de Jong, S · 1993
Earlier work this paper cites.
Support-vector networks
Cortes, C. & Vapnik, V · 1995
Earlier work this paper cites.
The lack of a priori distinctions between learning algorithms
Wolpert, D. H · 1996
Earlier work this paper cites.
The game of chess and searches in protein sequence space
Mandecki, W · 1998
Earlier work this paper cites.
Random forests
Breiman, L · 2001
Earlier work this paper cites.
Protein design is NP-hard
Pierce, N. A. & Winfree, E · 2002
Earlier work this paper cites.
Stochastic gradient boosting
Friedman, J. H · 2002
Earlier work this paper cites.
The spectrum kernel: A string kernel for SVM protein classification
Leslie, C., Eskin, E. & Noble, W. S · 2002
Earlier work this paper cites.
Optimizing the search algorithm for protein engineering by directed evolution
Fox, R. et al · 2003
Earlier work this paper cites.
Mismatch string kernels for discriminative protein classification
Leslie, C. S., Eskin, E., Cohen, A., Weston, J. & Noble, W. S · 2004
Earlier work this paper cites.
A neural-network-based method for predicting protein stability changes upon single point mutations
Capriotti, E., Fariselli, P. & Casadio, R · 2004
Earlier work this paper cites.
On the conservative nature of intragenic recombination
Drummond, D. A., Silberg, J. J., Meyer, M. M., Wilke, C. O. & Arnold, F. H · 2005
Earlier work this paper cites.
I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure
Capriotti, E., Fariselli, P. & Casadio, R · 2005
Earlier work this paper cites.
Predicting protein stability changes from sequences using support vector machines
Capriotti, E., Fariselli, P., Calabrese, R. & Casadio, R · 2005
Earlier work this paper cites.
The distribution of fitness effects among beneficial mutations in Fisher’s geometric model of adaptation
Orr, H. A · 2006
Earlier work this paper cites.
Prediction of protein stability changes for single-site mutations using support vector machines
Cheng, J., Randall, A. & Baldi, P · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning (MIT Press, 2006)
Rasmussen, C. E. & Williams, C. K. I · 2006
Earlier work this paper cites.
Improving catalytic function by ProSAR-driven enzyme evolution
Fox, R. J. et al · 2007
Earlier work this paper cites.
Engineering proteinase K using machine learning and synthetic genes
Liao, J. et al · 2007
Earlier work this paper cites.
A diverse family of thermostable cytochrome P450s created by recombination of stabilizing fragments
Li, Y. et al · 2007
Earlier work this paper cites.
AAindex: amino acid index database, progress report 2008
Kawashima, S. et al · 2007
Earlier work this paper cites.
A structural alignment kernel for protein structures
Qiu, J., Hue, M., Ben-Hur, A., Vert, J.-P. & Noble, W. S · 2007
Earlier work this paper cites.
The Elements of Statistical Learning; Data Mining, Inference and Prediction (Springer, New York, 2008)
Hastie, T. & Tibshirani, R · 2008
Earlier work this paper cites.
Exploring protein fitness landscapes by directed evolution
Romero, P. A. & Arnold, F. H · 2009
Earlier work this paper cites.
In silico characterization of protein chimeras: relating sequence and function within the same fold
Buske, F. A., Their, R., Gillam, E. M. & Bodén, M · 2009
Earlier work this paper cites.
Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0
Dehouck, Y. et al · 2009
Cited alongside, same era.
Gaussian process optimization in the bandit setting: No regret and experimental design (2009)
Srinivas, N., Krause, A., Kakade, S. M. & Seeger, M · 2009
Cited alongside, same era.
Enzyme promiscuity: a mechanistic and evolutionary perspective
Khersonsky, O. & Tawfik, D. S · 2010
Cited alongside, same era.
Predicting changes in protein thermostability brought about by single- or multi-site mutations
Tian, J., Wu, N., Chu, X. & Fan, Y · 2010
Cited alongside, same era.
A survey on transfer learning
Pan, S. J. & Yang, Q · 2010
Cited alongside, same era.
An exciting but challenging road ahead for computational enzyme design
Exploring sequence-function space of a poplar glutathione transferase using designed information-rich gene variants
Musdal, Y., Govindarajan, S. & Mannervik, B · 2017
Later among the works it cites.
Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization
Bedbrook, C. N., Yang, K. K., Rice, A. J., Gradinaru, V. & Arnold, F. H · 2017
Later among the works it cites.
Classification and Regression Trees (Routledge, 2017)
Breiman, L · 2017
Later among the works it cites.
Learning epistatic interactions from sequence-activity data to predict enantioselectivity
Zaugg, J., Gumulya, Y., Malde, A. K. & Bodén, M · 2017
Later among the works it cites.
DeepMHC: Deep convolutional neural networks for high-performance peptide-MHC binding affinity prediction 239236 (2017)
Hu, J. & Liu, Z · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Baker, D · 2010
Cited alongside, same era.
Machine learning, a probabilistic perspective (MIT Press, 2012)
Murphy, K · 2012
Cited alongside, same era.
PROTS-RF: a robust model for predicting mutation-induced protein stability changes
Li, Y. & Fang, J · 2012
Cited alongside, same era.
Grading amino acid properties increased accuracies of single point mutation on protein stability prediction
Liu, J. & Kang, X · 2012
Cited alongside, same era.
Three-dimensional structures of membrane proteins from genomic sequencing
Hopf, T. A. et al · 2012
Cited alongside, same era.
Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H. & Adams, R. P · 2012
Cited alongside, same era.
A few useful things to know about machine learning
Domingos, P · 2012
Cited alongside, same era.
DeepSite: protein-binding site predictor using 3D-convolutional neural networks
Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. & De Fabritiis, G · 2017
Later among the works it cites.
Atomic convolutional networks for predicting protein-ligand binding affinity (2017)
Gomes, J., Ramsundar, B., Feinberg, E. N. & Pande, V. S · 2017
Later among the works it cites.
Predicting protein binding affinity with word embeddings and recurrent neural networks 128223 (2017)
Mazzaferro, C · 2017
Later among the works it cites.
DeepLoc: prediction of protein subcellular localization using deep learning
Almagro Armenteros, J. J., Sønderby, C. K., Sønderby, S. K., Nielsen, H. & Winther, O · 2017
Later among the works it cites.
ProLanGO: Protein function prediction using neural machine translation based on a recurrent neural network
Cao, R. et al · 2017
Later among the works it cites.
dna2vec: Consistent vector representations of variable-length k-mers (2017)
Ng, P · 2017
Later among the works it cites.
UniProt: the universal protein knowledgebase
UniProt Consortium · 2017
Later among the works it cites.
A neural representation of sketch drawings (2017)
Ha, D. & Eck, D · 2017
Later among the works it cites.
Variational auto-encoding of protein sequences (2017)
Sinai, S., Kelsic, E., Church, G. M. & Nowak, M. A · 2017
Later among the works it cites.
Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics
Bedbrook, C. N., Yang, K. K., J, R. E., Viviana, G. & Arnold, F. H · 2018
Closest in time.
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
Jokinen, E., Heinonen, M. & Lähdesmäki, H · 2018
Closest in time.
A statistical model for improved membrane protein expression using sequence-derived features
Saladi, S. M., Javed, N., Müller, A. & Clemons, W. M · 2018
Closest in time.
Machine-learning-guided mutagenesis for directed evolution of fluorescent proteins
Saito, Y. et al · 2018
Closest in time.
DeepSol: A deep learning framework for sequence-based protein solubility prediction
Khurana, S. et al · 2018
Closest in time.
Near perfect protein multi-label classification with deep neural networks
Szalkai, B. & Grolmusz, V · 2018
Closest in time.
Improved descriptors for the quantitative structure–activity relationship modeling of peptides and proteins
Barley, M. H., Turner, N. J. & Goodacre, R · 2018
Closest in time.
Learned protein embeddings for machine learning
Yang, K., Wu, Z., Bedbrook, C. & Arnold, F · 2018
Closest in time.
Deep semantic protein representation for annotation, discovery, and engineering
Schwartz, A. S. et al · 2018
Closest in time.
Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics (2018)
Bedbrook, C. N., Yang, K. K., Robinson, J. E., Gradinaru, V. & Arnold, F. H · 2018
Closest in time.
A hierarchical latent vector model for learning long-term structure in music (2018)
Roberts, A., Engel, J., Raffel, C., Hawthorne, C. & Eck, D · 2018
Closest in time.
Deep generative models of genetic variation capture mutation effects
Riesselman, A. J., Ingraham, J. B. & Marks, D. S · 2018
Closest in time.
Recurrent neural network model for constructive peptide design
Müller, A. T., Hiss, J. A. & Schneider, G · 2018
Closest in time.
Gupta, A. & Zou, J · 2018
Closest in time.
Generative modeling for protein structures
Anand, N. & Huang, P · 2018
Closest in time.
Design by adaptive sampling (2018)
Brookes, D. H. & Listgarten, J · 2018
Closest in time.
Machine learning-assisted directed protein evolution with combinatorial libraries
Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J. & Arnold, F. H · 2019
Closest in time.
Unified rational protein engineering with sequence-only deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M. & Church, G. M · 2019
Closest in time.