Label-free SERS/machine learning procedures for protein classification

2021 ◽  
Author(s):  
Edoardo Farnesi ◽  
Andrea Barucci ◽  
Cristiano D'Andrea ◽  
Martina Banchelli ◽  
Chiara Amicucci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Classification ◽  
Label Free

scholarly journals mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Artur Yakimovich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Edge Label ◽  
Bacterial Colony ◽  
Link Type ◽  
Colony Counting ◽  
Anthrax Spores ◽  
Infection Biology

ABSTRACT Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

scholarly journals A Protein Classification Benchmark collection for machine learning

2007 ◽  
Vol 35 (Database) ◽  
pp. D232-D236 ◽  
Author(s):  
P. Sonego ◽  
M. Pacurar ◽  
S. Dhir ◽  
A. Kertesz-Farkas ◽  
A. Kocsor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Classification

Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency

2021 ◽  
pp. 000370282110345
Author(s):  
Tatu Rojalin ◽  
Dexter Antonio ◽  
Ambarish Kulkarni ◽  
Randy P. Carney
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Domain Knowledge ◽  
Collection Efficiency ◽  
Point Of Care ◽  
Automated Analysis ◽  
Downstream Processing ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Expert User

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

scholarly journals Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions

2018 ◽  
Vol 16 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Eoghan O'Duibhir ◽  
Jasmin Paris ◽  
Hannah Lawson ◽  
Catarina Sepulveda ◽  
Dahlia Doughty Shenton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Three Dimensions ◽  
Phenotypic Screening ◽  
Label Free

Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants

The Analyst ◽  
2021 ◽  
Author(s):  
Andrea Barucci ◽  
Cristiano D'Andrea ◽  
Edoardo Farnesi ◽  
Martina Banchelli ◽  
Chiara Amicucci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Label Free ◽  
Structural Determinants ◽  
Machine Learning Classification ◽  
Sers Detection ◽  
Biological Differences

We implement a machine learning classification of similar proteins by PCA mixed with multipeak fitting on SERS spectra for effective discrimination based on valid biological differences.

scholarly journals Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alessio Lugnan ◽  
Emmanuel Gooskens ◽  
Jeremy Vatin ◽  
Joni Dambre ◽  
Peter Bienstman
Keyword(s):  
Machine Learning ◽  
Computational Cost ◽  
Particle Analysis ◽  
Label Free ◽  
Machine Learning Approach ◽  
Microflow Cytometer ◽  
Learning Machine ◽  
Learning Issues ◽  
Low Computational Cost

AbstractMachine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of $${15.2}\,\upmu \text {m}$$ 15.2 μ m and $${18.6}\,\upmu \text {m}$$ 18.6 μ m . To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations.

CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning

Lab on a Chip ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 3888-3898 ◽  
Author(s):  
Domenico Rossi ◽  
David Dannhauser ◽  
Mariarosaria Telesco ◽  
Paolo A. Netti ◽  
Filippo Causa
Keyword(s):  
Machine Learning ◽  
Microfluidic Chip ◽  
Cell Counting ◽  
Learning Approach ◽  
Label Free ◽  
Biophysical Properties ◽  
Cd8 Cells ◽  
Cd8 T Lymphocyte ◽  
Machine Learning Approach ◽  
Compact Dimension

Human CD4+ and CD8+ cells are label-free investigated in a compact-dimension microfluidic chip for detailing biophysical properties. A machine learning approach on obtained results allows an accuracy of cell counting and classification up to 88%.

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