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14 - Machine Learning

from Part I - Physical Tools

Published online by Cambridge University Press:  12 December 2024

Thomas Andrew Waigh
Thomas Andrew Waigh
Affiliation:
University of Manchester
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Summary

Considers the application of machine learning and neural networks to the analysis of big data from bacterial experiments.

Keywords

AImachine learningbig data
Type
Chapter
Information
The Physics of Bacteria
From Cells to Biofilms
 , pp. 133 - 142
Publisher: Cambridge University Press
Print publication year: 2024

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References

Suggest Reading

Bishop, C. M., Pattern Recognition and Machine Learning. Springer: 2008. Accessible introduction to classical machine learning.Google Scholar
Chollet, F., Deep Learning with Python. Manning: 2018. Another practical introduction to neural network coding, slightly simpler than the Geron approach.Google Scholar
Geron, A., Hands on Machine Learning with Scikit-learn, Kera and TensorFlow. O’Reilly: 2019. The go to manual for practical aspects for coding machine learning algorithms and neural networks.Google Scholar
Goodfellow, I.; Bengio, Y.; Courville, A., Deep Learning: Adaptive Computation and Machine Learning. MIT Press: 2017. Good theoretical overview of deep learning including generative adversarial networks.Google Scholar
Murphy, K. P., Probabilistic Machine Learning: An Introduction. MIT Press: 2023. Excellent introduction to ML with an emphasis on Bayesian techniques.Google Scholar
Nielsen, A., Practical Time Series Analysis: Prediction with Statistics and Machine Learning. O’Reilly: 2019. Considers modern techniques to interpret time series data sets.Google Scholar
Zvelebil, M.; Baum, J. O., Understanding Bioinformatics. Garland Science: 2007. Excellent introduction to classical computational techniques in molecular biology.CrossRefGoogle Scholar

References

Bishop, C. M., Pattern Recognition and Machine Learning. Springer: 2006.Google Scholar
Goodfellow, I.; Bengio, Y.; Courville, A., Deep Learning. MIT Press: 2016.Google Scholar
Murphy, K. P., Probabilistic Machine Learning: An Introduction. MIT Press: 2022.Google Scholar
Jaynes, E. T.; Bretthorst, G. L., Probability Theory: The Logic of Science. Cambridge University Press: 2003.CrossRefGoogle Scholar
Cox, H.; Xu, H.; Waigh, T. A.; Lu, J. R., Single-molecule study of peptide gel dynamics reveals states of prestress. Langmuir 2018, 34 (48), 14678–14689.CrossRefGoogle ScholarPubMed
Cox, H.; Cao, M.; Xu, H.; Waigh, T. A.; Lu, J. R., Active modulation of states of prestress in self-assembled short peptide gels. Biomacromolecules 2019, 20 (4), 1719–1730.CrossRefGoogle ScholarPubMed
Bapst, V.; et al., Unveiling the predictive power of static structure in glassy systems. Nature Physics 2020, 16 (4), 448–454.CrossRefGoogle Scholar
Jumper, J.; et al., Highly accurate protein structure prediction with Alphafold. Nature 2021, 596 (7873), 583–589.CrossRefGoogle ScholarPubMed
Han, D.; Korabel, N.; Chen, R.; Johnston, M.; Gavrilova, A.; Allan, V. J.; Fedotov, S.; Waigh, T. A., Deciphering anomalous heterogeneous intracellular transport with neural networks. eLife 2020, 9, e52224.CrossRefGoogle ScholarPubMed
Drori, I., The Science of Deep Learning. Cambridge University Press: 2022.CrossRefGoogle Scholar
Geron, A., Hands-on Machine Learning with Scikit-learn, Keras and TensorFlow. O’Reilly: 2019.Google Scholar
Hartmann, R.; Singh, P. K.; Pearce, P.; Mok, R.; Song, B.; Diaz-Pascual, F.; Dunkel, J.; Drescher, K., Emergence of three-dimensional order and structure in growing biofilms. Nature Physics 2019, 15 (3), 251–256.CrossRefGoogle ScholarPubMed
Jeckel, H.; et al., Learning the space-time phase diagram of bacterial swarm expansion. Proceedings of the National Academy of Sciences of the United States of America 2019, 116 (5), 1489–1494.CrossRefGoogle ScholarPubMed
Dowling, J. E., The Retina: An Approachable Part of the Brain. Belknap Harvard: 2012.CrossRefGoogle Scholar
Newby, J. M.; Schaefer, A. M.; Lee, P. T.; Forest, M. G.; Lai, S. K., Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D. Proceedings of the National Academy of Sciences of the United States of America 2018, 115 (36), 9026–9031.CrossRefGoogle ScholarPubMed
Helgadottir, S.; Argua, A.; Volpe, G., Digital video microscopy enhanced by deep learning. Optica 2019, 6 (4), 506.CrossRefGoogle Scholar
Zhang, M.; Zhang, J.; Wang, Y.; Wang, J.; Achimovich, A. M.; Acton, S. T.; Gahlmann, A., Non-invasive single-cell morphometry in living bacterial biofilms. Nature Communications 2020, 11 (1), 6151.CrossRefGoogle ScholarPubMed
Korabel, N.; Waigh, T. A.; Fedotov, S.; Allan, V. J., Non-Markovian intracellular transport with sub-diffusion and run-length dependent detachment rate. PLOS One 2018, 13 (11), e0207436.CrossRefGoogle ScholarPubMed
Korabel, N.; Clemente, G. D.; Han, D.; Feldman, F.; Millard, T. H.; Waigh, T. A., Hemocytes in Drosophila melanogaster embryos move via heterogeneous anomalous diffusion. Communications Physics 2022, 5 (1), 269.CrossRefGoogle Scholar
Wang, H.; et al., Early detection and classification of live bacteria using time lapse coherent imaging and deep learning. Light: Science and Applications 2020, 9 (1), 118.CrossRefGoogle ScholarPubMed
Swinnen, I. A. M.; Bernaerts, K.; Dens, E. J. J.; Geeraerd, A. H.; van Impe, J. F., Predictive modelling of the microbial lag phase: A review. International Journal of Food Microbiology 2004, 94 (2), 137–159.CrossRefGoogle ScholarPubMed
Maquelin, K.; et al., Prospective study of the performance of vibrational spectroscopies for rapid identification of bacterial and fungal pathogens recovered from blood cultures. Journal of Clinical Microbiology 2003, 41 (1), 324–329.CrossRefGoogle ScholarPubMed
Tamiev, D.; Furman, P. E.; Reuel, N. F., Automated classification of bacterial cell sub-populations with CNNs. PLOS One 2020, 15 (10), e0241200.CrossRefGoogle Scholar
Sajedi, H.; Mohammadipanah, F.; Pashaei, A., Image-processing based taxonomy analysis of bacterial macromorphology using machine-learning model. Multimedia Tools and Applications 2020, 79 (43–44), 32711–32730.CrossRefGoogle Scholar
Sajedi, H.; Mohammadipanah, F.; Pashaei, A., Automated identification of myxobacterial genera using CNN. PLOS One 2019, 9 (1), 18238.Google Scholar
Dimauro, G.; Deperte, F.; Maglietta, R.; Bove, M.; La Gioia, F.; Reno, V.; Simone, L.; Gelardi, M., A novel approach for biofilm detection based on CNN. Electronics 2020, 9 (6), 881.CrossRefGoogle Scholar
Zvelebil, M. J.; Baum, J. O., Understanding Bioinformatics. Garland Science: 2007.CrossRefGoogle Scholar
Rychel, K.; Sastry, A. V.; Palsson, B. O., Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome. Nature Communications 2020, 11 (1), 6338.CrossRefGoogle ScholarPubMed
Yan, J.; Bhadra, P.; Li, A.; Sethiya, P.; Qin, L.; Tai, H. K.; Wong, K. H.; Siu, S. W. I., Deep-Am PEP30: Improve short antimicrobial peptide predictions with deep learning. Molecular Therapy: Nucleic Acids 2020, 20, 882–894.Google ScholarPubMed
Stokes, J. M.; et al., A deep learning approach to antibiotic discovery. Cell 2020, 180 (4), 688–702.CrossRefGoogle ScholarPubMed
Bendtsen, J. D.; Nielsen, H.; von Heijne, G.; Brunak, S., Improved prediction of signal peptides: Signal P 3.0. Journal of Molecular Biology 2004, 340 (4), 783–795.CrossRefGoogle Scholar
Fang, J.; Swain, A.; Unni, R.; Zhang, Y., Decoding optical data with machine learning. Lasers and Photonics Reviews 2021, 15 (2), 2000422.CrossRefGoogle ScholarPubMed
Goodacre, R.; Timmins, E. M.; Burton, R.; Kaderbhai, N.; Woodward, A. M.; Kell, D. B.; Rooney, P. J., Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology 1998, 144 (Pt 5), 1157–1170.CrossRefGoogle ScholarPubMed
Thrift, W. J.; et al., Deep learning analysis of vibrational spectra of bacterial lysate for rapid antimicrobial susceptibility testing. ACS Nano 2020, 14 (11), 15336–15348.CrossRefGoogle ScholarPubMed
Weis, C. V.; Jutzeler, C. R.; Borgwardt, K., Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: A systematic review. Clinical Microbiology and Infections 2020, 26 (10), 1310–1317.CrossRefGoogle ScholarPubMed
Mochalova, E. N.; Kotov, I. A.; Rozenberg, J. M.; Nikitin, M. P., Precise quantitative analysis of cell targeting by particle-based agents using imaging flow cytometry and convolutional neural network. Cytometry 2020, 97 (3), 279–287.CrossRefGoogle ScholarPubMed
Riekeles, M.; Schirmak, J.; Schulze-Makuch, D., Machine learning algorithms applied to identify microbial species by their motility. Life 2021, 11 (1), 44.CrossRefGoogle ScholarPubMed
Yu, S.; Li, H.; Li, X.; Fu, Y. V.; Liu, F., Classification of pathogens by Raman spectroscopy combined with generative adversarial networks. Science of the Total Environment 2020, 726, 138477.CrossRefGoogle ScholarPubMed
Foster, D., Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play. O’Reilly: 2019.Google Scholar
alphafold, G. Alpha fold protein structure database. alphafold.ebi.ac.uk.Google Scholar
Cichos, F.; Gustavsson, K.; Mehlig, B.; Volpe, G., Machine learning for active matter. Nature Machine Intelligence 2020, 2, 94–103.CrossRefGoogle Scholar
Hou, H.; Gan, T.; Yang, Y.; Zhu, X.; Liu, S.; Guo, W.; Hao, J., Using deep reinforcement learning to speed up collective cell migration. BMC Bioinformatics 2019, 20 (Suppl 18), 571.CrossRefGoogle ScholarPubMed
Baydin, A. G.; Pearlmutter, B. A.; Radul, A. A.; Siskind, J. M., Automatic differentiation in machine learning: A survey. Journal of Machine Learning Research 2018, 18 (153), 1–43.Google Scholar

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  • Machine Learning
  • Thomas Andrew Waigh, University of Manchester
  • Book: The Physics of Bacteria
  • Online publication: 12 December 2024
  • Chapter DOI: https://doi.org/10.1017/9781009313506.016
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Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Machine Learning
  • Thomas Andrew Waigh, University of Manchester
  • Book: The Physics of Bacteria
  • Online publication: 12 December 2024
  • Chapter DOI: https://doi.org/10.1017/9781009313506.016
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Machine Learning
  • Thomas Andrew Waigh, University of Manchester
  • Book: The Physics of Bacteria
  • Online publication: 12 December 2024
  • Chapter DOI: https://doi.org/10.1017/9781009313506.016
Available formats
×