The Immuno-Mass Spectrometry Chemical Microscope

Hare, Dominic J.; Shimoni, Olga; Bishop, David

doi:10.1016/j.trechm.2020.02.014

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Mass spectrometry uses a mass spectrometer to determine the mass-to-charge ratio (m/z) of the ions to be measured for analysis. Mass Spectrometry (MS) has been developed and applied for decades and is now widely used in the fields of analytical chemistry, biomedical research, environmental monitoring and material sample analysis [2][3][4][5] . A mass spectrometer generally consists of an injection system, an ion source, a mass analyser and a detector [6] .…”

Section: Introductionmentioning

confidence: 99%

A preprocessing study of serum mass spectrometry data using machine learning as a base

Han,

Dong,

Liang

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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Mass spectrometry data preprocessing is the most basic and important part of mass spectrometry data application. Aiming at the problems of high dimension of serum mass spectrometry data, an efficient and usable preprocessing method is proposed which deals with the inconsistency of dimensions in each group, non-normalization, and non-unique attribute columns. Firstly, the method of rounding down the Mass value is used to determine the attribute column name; Secondly, four ways are used to determine the unique value for the problem that the existence of the same Mass term after rounding down the Mass value; then four ways of filling the missing values are done, and then the transposition of the overall data is carried out. Since four treatments are done for both the same Mass term and missing values, then 16 permutations are available, with a total of 16 new datasets; Finally, three machine learning methods are used to demonstrate the performances of different data preprocessing approaches on the datasets, which are SVM(linear), Random Forest and Logistic Regression. The numerical results show that the accuracies of classification of the three classifiers respectively reach 0.8296, 0.8906, and 0.8088, which validate that the preprocessing algorithms proposed in this paper can efficiently process the raw mass spectrometry data into valid and normalized data.

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Section: Introductionmentioning

confidence: 99%

A preprocessing study of serum mass spectrometry data using machine learning as a base

Han,

Dong,

Liang

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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“…22 Imaging applications were rst described in 1994, 23 and the technology has matured into a key component of the broader mass spectrometry imaging eld. 24,25 Applications for LA-ICP-MS imaging in cancer research have grown steadily over the last decade, ranging from observational studies of in 26 and ex vivo27 tumour chemical microenvironments to the development and commercialisation of time-of-ight LA-ICP-MS instruments for high-dimensional multiplexed immunoimaging 28 and precision oncology. 29 Here, in this proof of concept study, we present a completely new approach to supporting cancer detection using LA-ICP-MS that is inspired by recent innovations in chemical imaging that fuse automated feature detection and molecular mass spectrometry imaging.…”

Section: Introductionmentioning

confidence: 99%