scholarly journals Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification

The Analyst ◽  
2021 ◽  
Author(s):  
Nicolas Pavillon ◽  
Nicholas I Smith
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Protein Synthesis ◽  
Biological Samples ◽  
Molecular Information ◽  
Molecular Indicators ◽  
Sparse Classification

Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use this large amount of...

Raman spectroscopy and machine learning for the classification of breast cancers

2021 ◽  
pp. 120300
Author(s):  
Lihao Zhang ◽  
Chengjian Li ◽  
Di Peng ◽  
Xiaofei Yi ◽  
Shuai He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Breast Cancers

Analysis of handmade paper by Raman spectroscopy combined with machine learning

2021 ◽  
Author(s):  
Chunsheng Yan ◽  
Zhongyi Cheng ◽  
Si Luo ◽  
Chen Huang ◽  
Songtao Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Handmade Paper

The Application of Machine-Learning and Raman Spectroscopy for the Rapid Detection of Edible Oils Type and Adulteration

2021 ◽  
pp. 131471
Author(s):  
Hefei Zhao ◽  
Yinglun Zhan ◽  
Zheng Xu ◽  
Joshua John Nduwamungu ◽  
Yuzhen Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Rapid Detection ◽  
Edible Oils

A Study on Generating Fake Images of Podocyte Cells Based on Acgans with Restriction of Learning

2020 ◽  
Vol 38 ◽  
pp. 76-82
Author(s):  
Yusuke Ono ◽  
Tsutomu Matsuura ◽  
Toshiyuki Matsuzaki ◽  
Keiju Hiromura ◽  
Takeo Aoki
Keyword(s):  
Machine Learning ◽  
Epithelial Cells ◽  
Conventional Method ◽  
Biological Samples ◽  
Previous Method ◽  
Glomerular Epithelial Cells ◽  
Passage Number

In general, we need a lot of data for improving the accuracy of machine learning. However, the number of biological samples what we can obtain are not enough for machine learning. This problem exists in the classification of glomerular epithelial cells with the progress of disease, and its accuracy is contrary to our intuitive impression. Therefore, we would like to improve the accuracy by generating a lot of fake images using Generative Adversarial Nets (GANs). About podocyte cells, it was difficult to obtain an arbitrary disease by previous method. In this paper, we propose the model with restriction of learning by shapes information based on ACGANs, and we investigate how much fake images generated by our method are similar to real images. According to the results, the passage number of fake images by our method is 17% higher than conventional method.

Decoding Phases of Matter by Machine-Learning Raman Spectroscopy

2019 ◽  
Vol 12 (5) ◽  
Author(s):  
Anyang Cui ◽  
Kai Jiang ◽  
Minhong Jiang ◽  
Liyan Shang ◽  
Liangqing Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy

scholarly journals Intraoperative Detection of Pediatric Brain Tumor Margins Using Raman Spectroscopy.

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Rashad Jabarkheel ◽  
Jonathon J Parker ◽  
Chi-Sing Ho ◽  
Travis Shaffer ◽  
Sanjiv Gambhir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Tissue ◽  
Pediatric Brain Tumor ◽  
Tissue Samples ◽  
Tumor Margins ◽  
Pediatric Brain ◽  
Tumor Debulking

Abstract INTRODUCTION Surgical resection is a mainstay of treatment in patients with brain tumors both for tissue diagnosis and for tumor debulking. While maximal resection of tumors is desired, neurosurgeons can be limited by the challenge of differentiating normal brain from tumor using only microscopic visualization and tactile feedback. Additionally, intraoperative decision-making regarding how aggressively to pursue a gross total resection frequently relies on pathologic preliminary diagnosis using frozen sections which are both time consuming and fallible. Here, we investigate the potential for Raman spectroscopy (RS) to rapidly detect pediatric brain tumor margins and classify brain tissue samples equivalent to histopathology. METHODS Using a first-of-its-kind rapid acquisition RS device we intraoperatively imaged fresh ex vivo pediatric brain tissue samples (2-3 mm × 2-3 mm × 2-3 mm) at the Lucille Packard Children's Hospital. All imaged samples received standard final histopathological analysis, as RS is a nondestructive imaging technique. We curated a labeled dataset of 575 + unique Raman spectra gathered from 160 + brain samples resulting from 23 pediatric patients who underwent brain tissue resection as part of tumor debulking or epilepsy surgery (normal controls). RESULTS To our knowledge we have created the largest labeled Raman spectra dataset of pediatric brain tumors. We are developing an end-to-end machine learning model that can predict final histopathology diagnosis within minutes from Raman spectral data. Our preliminary principle component analyses suggest that RS can be used to classify various brain tumors similar to “frozen” histopathology and can differentiate normal from malignant brain tissue in the context of low-grade glioma resections. CONCLUSION Our work suggests that machine learning approaches can be used to harness the material identification properties of RS for classifying brain tumors and detecting their margins.

Machine Learning-Assisted Raman Spectroscopy for pH and Lactate Sensing in Body Fluids

2020 ◽  
Vol 92 (20) ◽  
pp. 13888-13895
Author(s):  
Ion Olaetxea ◽  
Ana Valero ◽  
Eneko Lopez ◽  
Héctor Lafuente ◽  
Ander Izeta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Body Fluids

Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy

The Analyst ◽  
2020 ◽  
Vol 145 (14) ◽  
pp. 4827-4835 ◽  
Author(s):  
Shizhuang Weng ◽  
Hecai Yuan ◽  
Xueyan Zhang ◽  
Pan Li ◽  
Ling Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Deep Learning ◽  
Surface Enhanced Raman Spectroscopy ◽  
Learning Networks ◽  
Biological Detection ◽  
Machine Learning Methods ◽  
Intelligent Analysis ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis.

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