scholarly journals Supervised machine learning in the mass spectrometry laboratory: A tutorial

2021 ◽  
Author(s):  
Edward S. Lee ◽  
Thomas J.S. Durant
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Supervised Machine Learning

scholarly journals Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandre Maciel-Guerra ◽  
Necati Esener ◽  
Katharina Giebel ◽  
Daniel Lea ◽  
Martin J. Green ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Treatment Success ◽  
Clinical Mastitis ◽  
Supervised Machine Learning ◽  
Structural Protein ◽  
Cohen’S Kappa ◽  
Maldi Tof ◽  
Streptococcus Uberis ◽  
Cohen's Kappa

AbstractStreptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen’s kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen’s kappa to 92.2% and 84.1% respectively. A computational framework integrating protein–protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.

scholarly journals Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Metabolites ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 243 ◽  
Author(s):  
Ulf W. Liebal ◽  
An N. T. Phan ◽  
Malvika Sudhakar ◽  
Karthik Raman ◽  
Lars M. Blank
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Data Analysis ◽  
Metabolic Engineering ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

scholarly journals Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5388
Author(s):  
Paul Mittal ◽  
Mark R. Condina ◽  
Manuela Klingler-Hoffmann ◽  
Gurjeet Kaur ◽  
Martin K. Oehler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Histopathological Examination ◽  
Supervised Machine Learning ◽  
Charge Ratio ◽  
Cancer Tissue ◽  
Ionization Mass ◽  
Colorectal Tumour ◽  
Cancer Diagnostic

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.

scholarly journals Bi-modal Variational Autoencoders for Metabolite Identification Using Tandem Mass Spectrometry

2021 ◽  
Author(s):  
Svetlana Kutuzova ◽  
Christian Igel ◽  
Mads Nielsen ◽  
Douglas McCloskey
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Metabolite Identification ◽  
Supervised Machine Learning ◽  
Grand Challenge ◽  
Tandem Mass ◽  
Generative Modeling ◽  
General Rules ◽  
Latent Representations

A grand challenge of analytical chemistry is the identification of unknown molecules based on tandem mass spectrometry (MS/MS) spectra. Current metabolite annotation approaches are often manual or partially automated, and commonly rely on a spectral database to search from or train machine learning classifiers on. Unfortunately, spectral databases are often instrument specific and incomplete due to the limited availability of compound standards or a molecular database, which limits the ability of methods utilizing them to predict novel molecule structures. We describe a generative modeling approach that can leverage the vast amount of unpaired and/or unlabeled molecule structures and MS/MS spectra to learn general rules for synthesizing molecule structures and MS/MS spectra. The approach is based on recent work using semi-supervised deep variational autoencoders to learn joint latent representations of multiple and complex modalities. We show that adding molecule structures with no spectra to the training set improves the prediction quality on spectra from a structure disjoint dataset of new molecules, which is not possible using bi-modal supervised approaches. The described methodology provides a demonstration and framework for how recent advances in semi-supervised machine learning can be applied to overcome bottlenecks in missing annotations and noisy data to tackle unaddressed problems in the life sciences where large volumes of data are available.

scholarly journals SLIMP: Supervised learning of metabolite-protein interactions from co-fractionation mass spectrometry data

2021 ◽  
Author(s):  
Boris M. Zühlke ◽  
Ewelina M. Sokolowska ◽  
Marcin Luzarowski ◽  
Denis Schlossarek ◽  
Monika Chodasiewicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Protein Interactions ◽  
Mass Spectrometry Data ◽  
Supervised Machine Learning ◽  
Growth Stages ◽  
Data Sets ◽  
Learning Approaches ◽  
Proteomic Data ◽  
A Genome

AbstractMetabolite-protein interactions affect and shape diverse cellular processes. Yet, despite advances, approaches for identifying metabolite-protein interactions at a genome-wide scale are lacking. Here we present an approach termed SLIMP that predicts metabolite-protein interactions using supervised machine learning on features engineered from metabolic and proteomic profiles from a co-fractionation mass spectrometry-based technique. By applying SLIMP with gold standards, assembled from public databases, along with metabolic and proteomic data sets from multiple conditions and growth stages we predicted over 9,000 and 20,000 metabolite-protein interactions for Saccharomyces cerevisiae and Arabidopsis thaliana, respectively. Extensive comparative analyses corroborated the quality of the predictions from SLIMP with respect to widely-used performance measures (e.g. F1-score exceeding 0.8). SLIMP predicted novel targets of 2’, 3’ cyclic nucleotides and dipeptides, which we analysed comparatively between the two organisms. Finally, predicted interactions for the dipeptide Tyr-Asp in Arabidopsis and the dipeptide Ser-Leu in yeast were independently validated, opening the possibility for future applications of supervised machine learning approaches in this area of systems biology.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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