1.
Zhou, Y, Li, G, Dong, J, Xing, XH, Dai, J, Zhang, C. MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae. Metab Eng. 2018;47:294-302.
Google Scholar |
Crossref |
Medline2.
Romero, PA, Krause, A, Arnold, FH. Navigating the protein fitness landscape with Gaussian processes. Proc Natl Acad Sci USA. 2013;110:E193-E201.
Google Scholar |
Crossref3.
Zelezniak, A, Vowinckel, J, Capuano, F, et al. Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts. Cell Syst. 2018;7:269-283.e6.
Google Scholar |
Medline4.
Zrimec, J, Lapanje, A. DNA structure at the plasmid origin-of-transfer indicates its potential transfer range. Sci Rep. 2018;8:1820.
Google Scholar |
Crossref |
Medline5.
Zrimec, J, Börlin, CS, Buric, F, et al. Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure. Nat Commun. 2020;11:6141.
Google Scholar |
Crossref |
Medline6.
Hastie, T, Tibshirani, R, Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer Science & Business Media; 2013.
Google Scholar7.
He, K, Zhang, X, Ren, S, Sun, J. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. Paper presented at: Proceedings of the IEEE International Conference on Computer Vision; December 7-13, 2015:1026-1034; Santiago, Chile.
https://ieeexplore.ieee.org/document/7410480.
Google Scholar8.
Liu, X, Faes, L, Kale, AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1:e271-e297.
Google Scholar |
Crossref |
Medline9.
Ripley, BD. Pattern Recognition and Neural Networks. Cambridge, UK: Cambridge University Press; 2007.
Google Scholar10.
Domingos, P. A few useful things to know about machine learning. Commun ACM. 2012;55:78-87.
Google Scholar |
Crossref |
ISI11.
Botchkarev, A. A new typology design of performance metrics to measure errors in machine learning regression algorithms. IJIKM. 2019;14:45-76.
Google Scholar |
Crossref12.
Tsimring, LS. Noise in biology. Rep Prog Phys. 2014;77:026601.
Google Scholar |
Crossref |
Medline13.
Harris, EF, Smith, RN. Accounting for measurement error: a critical but often overlooked process. Arch Oral Biol. 2009;54:S107-S117.
Google Scholar |
Crossref |
Medline |
ISI14.
Bruggeman, FJ, Teusink, B. Living with noise: on the propagation of noise from molecules to phenotype and fitness. Curr Opin Syst Biol. 2018;8:144-150.
Google Scholar |
Crossref15.
Benevenuta, S, Fariselli, P. On the upper bounds of the real-valued predictions. Bioinform Biol Insights. 2019;13:1177932219871263.
Google Scholar |
SAGE Journals16.
Montanucci, L, Martelli, PL, Ben-Tal, N, Fariselli, P. A natural upper bound to the accuracy of predicting protein stability changes upon mutations. Bioinformatics. 2019;35:1513-1517.
Google Scholar |
Crossref |
Medline17.
Jeske, L, Placzek, S, Schomburg, I, Chang, A, Schomburg, D. BRENDA in 2019: a European ELIXIR core data resource. Nucleic Acids Res. 2019;47:D542-D549.
Google Scholar |
Crossref |
Medline18.
Engqvist, MKM . Correlating enzyme annotations with a large set of microbial growth temperatures reveals metabolic adaptations to growth at diverse temperatures. BMC Microbiol. 2018;18:177.
Google Scholar |
Crossref |
Medline19.
Cherry, JM, Hong, EL, Amundsen, C, et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 2012;40:D700-D705.
Google Scholar |
Crossref20.
Cherry, JM, Adler, C, Ball, C, et al. SGD: Saccharomyces Genome Database. Nucleic Acids Res. 1998;26:73-79.
Google Scholar |
Crossref |
Medline |
ISI21.
Xu, Z, Wei, W, Gagneur, J, et al. Bidirectional promoters generate pervasive transcription in yeast. Nature. 2009;457:1033-1037.
Google Scholar |
Crossref |
Medline22.
Nagalakshmi, U, Wang, Z, Waern, K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320:1344-1349.
Google Scholar |
Crossref |
Medline |
ISI23.
Ziemann, M, Kaspi, A, El-Osta, A. Digital expression explorer 2: a repository of uniformly processed RNA sequencing data. GigaScience. 2019;8:giz022.
Google Scholar |
Crossref |
Medline24.
Li, B, Ruotti, V, Stewart, RM, Thomson, JA, Dewey, CN. RNA-seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010;26:493-500.
Google Scholar |
Crossref |
Medline |
ISI25.
Box, GEP, Cox, DR. An analysis of transformations. J R Stat Soc Series B Stat Methodol. 1964;26:211-243.
Google Scholar |
Crossref |
ISI26.
Li, G, Ji, B, Nielsen, J. The pan-genome of Saccharomyces cerevisiae. FEMS Yeast Res. 2019;19:foz064.
Google Scholar |
Crossref27.
Peter, J, De Chiara, M, Friedrich, A, et al. Genome evolution across 1,011 Saccharomyces cerevisiae isolates. Nature. 2018;556:339-344.
Google Scholar |
Crossref |
Medline28.
Chen, Z, Zhao, P, Li, F, et al. iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics. 2018;34:2499-2502.
Google Scholar |
Crossref |
Medline29.
Alley, EC, Khimulya, G, Biswas, S, AlQuraishi, M, Church, GM. Unified rational protein engineering with sequence-based deep representation learning. Nat Methods. 2019;16:1315-1322. doi:
10.1038/s41592-019-0598-1. Google Scholar |
Crossref |
Medline30.
Suzek, BE, Wang, Y, Huang, H, et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics. 2015;31:926-932.
Google Scholar |
Crossref |
Medline31.
Pedregosa, F, Varoquaux, G, Gramfort, A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825-2830.
Google Scholar |
ISI32.
LeCun, Y, Haffner, P, Bottou, L, Bengio, Y. Object recognition with gradient-based learning. In: Forsyth DA, Mundy JL, di Gesú V, Cipolla R , eds. Shape, Contour and Grouping in Computer Vision. Berlin, Germany: Springer; 1999:319-345.
Google Scholar33.
Hou, J, Adhikari, B, Cheng, J. DeepSF: deep convolutional neural network for mapping protein sequences to folds. Bioinformatics. 2018;34:1295-1303.
Google Scholar |
Crossref |
Medline34.
Ioffe, S, Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv. 2015.
https://arxiv.org/pdf/1502.03167.pdf.
Google Scholar35.
Srivastava, N, Hinton, G, Krizhevsky, A, Sutskever, I, Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15:1929-1958.
Google Scholar |
ISI36.
Krizhevsky, A, Sutskever, I, Hinton, GE. ImageNet classification with deep convolutional neural networks. In: Pereira, F, Burges, CJC, Bottou, L, Weinberger, KQ, eds. Advances in Neural Information Processing Systems 25. Red Hook, NY: Curran Associates, Inc.; 2012:1097-1105.
Google Scholar37.
Kingma, DP, Ba, J. Adam: a method for stochastic optimization. arXiv. 2014.
https://arxiv.org/pdf/1412.6980.pdf.
Google Scholar38.
Nair, V, Hinton, GE. Rectified linear units improve restricted Boltzmann machines. Paper presented at: Proceedings of the 27th International Conference on Machine Learning (ICML-10); :807-814; Haifa, Israel.
https://www.cs.toronto.edu/~hinton/absps/reluICML.pdf.
Google Scholar39.
Bergstra, J, Komer, B, Eliasmith, C, Yamins, D, Cox, DD. Hyperopt: a Python library for model selection and hyperparameter optimization. Comput Sci Discov. 2015;8:014008.
Google Scholar |
Crossref40.
Bengio, Y . Practical recommendations for gradient-based training of deep architectures. In: Montavon, G, Orr, GB, Müller, K-R eds. Neural Networks: Tricks of the Trade. 2nd ed. Berlin, Germany: Springer; 2012:437-478.
Google Scholar41.
Bergstra, JS, Bardenet, R, Bengio, Y, Kégl, B. Algorithms for hyper-parameter optimization. In: Shawe-Taylor, J, Zemel, RS, Bartlett, PL, Pereira, F, Weinberger, KQeds. Advances in Neural Information Processing Systems 24. Red Hook, NY: Curran Associates, Inc.; 2011:2546-2554.
Google Scholar42.
Li, G, Rabe, KS, Nielsen, J, Engqvist, MKM. Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth Biol. 2019;8:1411-1420.
Google Scholar |
Crossref |
Medline43.
Lombard, V, Golaconda Ramulu, H, Drula, E, Coutinho, PM, Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490-D495.
Google Scholar |
Crossref |
Medline44.
Matek, C, Schwarz, S, Spiekermann, K, Marr, C. Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nat Mach Intell. 2019;1:538-544.
Google Scholar |
Crossref45.
Dodge, S, Karam, L. A study and comparison of human and deep learning recognition performance under visual distortions. Paper presented at: 2017 26th International Conference on Computer Communication and Networks (ICCCN); ; Vancouver, BC, Canada. doi:
10.1109/icccn.2017.8038465. Google Scholar |
Crossref46.
Singh, AV, Maharjan, RS, Kanase, A, et al. Machine-learning-based approach to decode the influence of nanomaterial properties on their interaction with cells. ACS Appl Mater Interfaces. 2021;13:1943-1955.
Google Scholar |
Crossref |
Medline47.
Singh, AV, Rosenkranz, D, Ansari, MH, et al. Artificial intelligence and machine learning empower advanced biomedical material design to toxicity prediction. Adv Intell Syst. 2020;2:2000084.
Google Scholar |
Crossref48.
Smola, AJ, Schölkopf, B. A tutorial on support vector regression. Stat Comput. 2004;14:199-222.
Google Scholar |
Crossref |
ISI49.
Cheng, J, Maier, KC, Avsec Rus, ŽP, Gagneur, J. Cis-regulatory elements explain most of the mRNA stability variation across genes in yeast. RNA. 2017;23:1648-1659.
Google Scholar |
Crossref |
Medline50.
Singh, AV, Ansari, MHD, Rosenkranz, D, et al. Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine. Adv Healthc Mater. 2020;9:e1901862.
Google Scholar |
Crossref51.
Jelier, R, Semple, JI, Garcia-Verdugo, R, Lehner, B. Predicting phenotypic variation in yeast from individual genome sequences. Nat Genet. 2011;43:1270-1274.
Google Scholar |
Crossref |
Medline52.
Leuenberger, P, Ganscha, S, Kahraman, A, et al. Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability. Science. 2017;355:eaai7825.
Google Scholar |
Crossref |
Medline
留言 (0)