A New Wavelet-Based Approach for Mass Spectrometry Data Classification

2018 ◽  
pp. 175-189
Author(s):  
Achraf Cohen ◽  
Chaimaa Messaoudi ◽  
Hassan Badir
Keyword(s):  
Mass Spectrometry ◽  
Data Classification ◽  
Mass Spectrometry Data

scholarly journals Towards CNN Representations for Small Mass Spectrometry Data Classification: From Transfer Learning to Cumulative Learning

2020 ◽  
Author(s):  
Khawla Seddiki ◽  
Philippe Saudemont ◽  
Frédéric Precioso ◽  
Nina Ogrinc ◽  
Maxence Wisztorski ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Data Classification ◽  
Representation Learning ◽  
Mass Spectrometry Data ◽  
Training Dataset ◽  
Require Time ◽  
Training Samples

AbstractRapid and accurate clinical diagnosis of pathological conditions remains highly challenging. A very important component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some popular Machine Learning (ML) approaches have been investigated for this purpose but these ML models require time-consuming preprocessing steps such as baseline correction, denoising, and spectrum alignment to remove non-sample-related data artifacts. They also depend on the tedious extraction of handcrafted features, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn efficient representations from raw data without the need for costly preprocessing. However, their effectiveness drastically decreases when the number of available training samples is small, which is a common situation in medical applications. Transfer learning strategies extend an accurate representation model learnt usually on a large dataset containing many categories, to a smaller dataset with far fewer categories. In this study, we first investigate transfer learning on a 1D-CNN we have designed to classify MS data, then we develop a new representation learning method when transfer learning is not powerful enough, as in cases of low-resolution or data heterogeneity. What we propose is to train the same model through several classification tasks over various small datasets in order to accumulate generic knowledge of what MS data are, in the resulting representation. By using rat brain data as the initial training dataset, a representation learning approach can have a classification accuracy exceeding 98% for canine sarcoma cancer cells, human ovarian cancer serums, and pathogenic microorganism biotypes in 1D clinical datasets. We show for the first time the use of cumulative representation learning using datasets generated in different biological contexts, on different organisms, in different mass ranges, with different MS ionization sources, and acquired by different instruments at different resolutions. Our approach thus proposes a promising strategy for improving MS data classification accuracy when only small numbers of samples are available as a prospective cohort. The principles demonstrated in this work could even be beneficial to other domains (astronomy, archaeology…) where training samples are scarce.

scholarly journals Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Khawla Seddiki ◽  
Philippe Saudemont ◽  
Frédéric Precioso ◽  
Nina Ogrinc ◽  
Maxence Wisztorski ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Data Classification ◽  
Small Mass ◽  
Mass Spectrometry Data ◽  
Training Dataset ◽  
Learning Approaches ◽  
Require Time ◽  
Cumulative Learning

AbstractRapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available.

154: Integration of TPSA and High-Throughput Mass Spectrometry Data Improves Prostate Cancer Prediction

2007 ◽  
Vol 177 (4S) ◽  
pp. 52-53
Author(s):  
Stefano Ongarello ◽  
Eberhard Steiner ◽  
Regina Achleitner ◽  
Isabel Feuerstein ◽  
Birgit Stenzel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data ◽  
Cancer Prediction

Nonparametric Pre-Processing Methods and Inference Tools for Analyzing Time-of-Flight Mass Spectrometry Data.

2007 ◽  
Vol 3 (2) ◽  
pp. 127-147 ◽  
Author(s):  
Anestis Antoniadis ◽  
Jeremie Bigot ◽  
Sophie Lambert-Lacroix ◽  
Frederique Letue
Keyword(s):  
Mass Spectrometry ◽  
Time Of Flight ◽  
Mass Spectrometry Data ◽  
Processing Methods ◽  
Flight Mass Spectrometry

Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability

2021 ◽  
Author(s):  
Trevor N. Clark ◽  
Joëlle Houriet ◽  
Warren S. Vidar ◽  
Joshua J. Kellogg ◽  
Daniel A. Todd ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Interlaboratory Comparison ◽  
Mass Spectrometry Data ◽  
Underlying Causes

TopPIC Gateway: A Web Gateway for Top-Down Mass Spectrometry Data Interpretation

2020 ◽  
Author(s):  
In Kwon Choi ◽  
Eroma Abeysinghe ◽  
Eric Coulter ◽  
Suresh Marru ◽  
Marlon Pierce ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Interpretation ◽  
Mass Spectrometry Data ◽  
Top Down ◽  
Top Down Mass Spectrometry

scholarly journals mzML—a Community Standard for Mass Spectrometry Data

2010 ◽  
Vol 10 (1) ◽  
pp. R110.000133 ◽  
Author(s):  
Lennart Martens ◽  
Matthew Chambers ◽  
Marc Sturm ◽  
Darren Kessner ◽  
Fredrik Levander ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Community Standard
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