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 Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis

2021 ◽  
Vol 17 (6) ◽  
pp. e1009108
Author(s):  
Necati Esener ◽  
Alexandre Maciel Guerra ◽  
Katharina Giebel ◽  
Daniel Lea ◽  
Martin J. Green ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mass Spectrometry ◽  
Staphylococcus Aureus ◽  
Logistic Regression ◽  
Bovine Mastitis ◽  
Cohen’S Kappa ◽  
Mlp Neural Network ◽  
Cohen's Kappa ◽  
Linear Svm

Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation–Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen’s kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen’s kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen’s kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach

2019 ◽  
Vol 32 (3) ◽  
pp. 381-392 ◽  
Author(s):  
Sabela C. Mallo ◽  
Sonia Valladares-Rodriguez ◽  
David Facal ◽  
Cristina Lojo-Seoane ◽  
Manuel J. Fernández-Iglesias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severity Score ◽  
Performance Metrics ◽  
Neuropsychiatric Symptoms ◽  
Depression Scale ◽  
Forest Plot ◽  
Total Stress ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Stress Score

ABSTRACTObjectives:To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.Design:Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1 score, and Cohen’s kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm.Setting:Primary care health centers.Participants:128 participants: 78 cognitively unimpaired and 50 with MCI.Measurements:Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score.Results:30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen’s kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score.Conclusions:ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI.

scholarly journals Rapid classification and prediction of COVID-19 severity by MALDI-TOF mass spectrometry analysis of serum peptidome

2020 ◽  
Author(s):  
Rosa M. Gomila ◽  
Gabriel Martorell ◽  
Pablo A. Fraile-Ribot ◽  
Antonio Doménech-Sánchez ◽  
Antonio Oliver ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Favourable Outcome ◽  
Mass Spectrometry Analysis ◽  
Learning Approaches ◽  
Maldi Tof Ms ◽  
Spectrometry Analysis ◽  
Maldi Tof ◽  
Critical Patients ◽  
Tof Ms

ABSTRACTClassification and early detection of severe COVID-19 patients is urgently required to establish an effective treatment. Here, we tested the utility of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to classify and predict the severity of COVID-19 in a clinical setting. We used this technology to analyse the mass spectra profiles of the sera from 80 COVID-19 patients, clinically classified as mild (33), severe (26) and critical (21), and 20 healthy controls. We found a clear variability of the serum peptidome profile depending on COVID-19 severity. Seventy-eight peaks were significantly different and 12 at least four fold more intense in the set of critical patients than in the mild ones. Analysis of the resulting matrix of peak intensities by machine learning approaches classified severe (severe and critical) and non-severe (mild) patients with a 90% of accuracy. Furthermore, machine learning predicted correctly the favourable outcome of the severe patients in 85% of the cases and the unfavourable in 38% of the cases. Finally, liquid chromatography mass spectrometry analysis of sera identified five proteins that were significantly upregulated in the critical patients. They included serum amyloid proteins A1 and A2, which probably yielded the most intense peaks with m/z 11,530 and 11,686 detected by MALDI-TOF MS.In summary, we demonstrated the potential of the MALDI-TOF MS as a bench to bedside technology to aid clinicians in their decisions to classify COVID-19 patients and predict their evolution.

scholarly journals Episodix: a serious game to detect cognitive impairment in senior adults. A psychometric study

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5478 ◽  
Author(s):  
Sonia Valladares-Rodriguez ◽  
Manuel J. Fernández-Iglesias ◽  
Luis Anido-Rifón ◽  
David Facal ◽  
Roberto Pérez-Rodríguez
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Performance Metrics ◽  
Cognitive Impairments ◽  
Senior Adults ◽  
Cohen’S Kappa ◽  
Machine Learning Classification ◽  
Cohen's Kappa ◽  
Preliminary Study ◽  
Psychometric Validity

Introduction Assessment of episodic memory is traditionally used to evaluate potential cognitive impairments in senior adults. The present article discusses the capabilities of Episodix, a game to assess the aforementioned cognitive area, as a valid tool to discriminate among mild cognitive impairment (MCI), Alzheimer’s disease (AD) and healthy individuals (HC); that is, it studies the game’s psychometric validity study to assess cognitive impairment. Materials and Methods After a preliminary study, a new pilot study, statistically significant for the Galician population, was carried out from a cross-sectional sample of senior adults as target users. A total of 64 individuals (28 HC, 16 MCI, 20 AD) completed the experiment from an initial sample of 74. Participants were administered a collection of classical pen-and-paper tests and interacted with the games developed. A total of six machine learning classification techniques were applied and four relevant performance metrics were computed to assess the classification power of the tool according to participants’ cognitive status. Results According to the classification performance metrics computed, the best classification result is obtained using the Extra Trees Classifier (F1 = 0.97 and Cohen’s kappa coefficient = 0.97). Precision and recall values are also high, above 0.9 for all cognitive groups. Moreover, according to the standard interpretation of Cohen’s kappa index, classification is almost perfect (i.e., 0.81–1.00) for the complete dataset for all algorithms. Limitations Weaknesses (e.g., accessibility, sample size or speed of stimuli) detected during the preliminary study were addressed and solved. Nevertheless, additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. Conclusion Promising results obtained about psychometric validity of Episodix, represent a relevant step ahead towards the introduction of serious games and machine learning in regular clinical practice for detecting MCI or AD. However, more research is needed to explore the introduction of item response theory in this game and to obtain the required normative data for clinical validity.

scholarly journals Patient-Specific Explanations for Predictions of Clinical Outcomes

ACI Open ◽  
2019 ◽  
Vol 03 (02) ◽  
pp. e88-e97
Author(s):  
Mohammadamin Tajgardoon ◽  
Malarkodi J. Samayamuthu ◽  
Luca Calzoni ◽  
Shyam Visweswaran
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Kappa Coefficient ◽  
Patient Specific ◽  
P Value ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Cohen’S Kappa Coefficient

Abstract Background Machine learning models that are used for predicting clinical outcomes can be made more useful by augmenting predictions with simple and reliable patient-specific explanations for each prediction. Objectives This article evaluates the quality of explanations of predictions using physician reviewers. The predictions are obtained from a machine learning model that is developed to predict dire outcomes (severe complications including death) in patients with community acquired pneumonia (CAP). Methods Using a dataset of patients diagnosed with CAP, we developed a predictive model to predict dire outcomes. On a set of 40 patients, who were predicted to be either at very high risk or at very low risk of developing a dire outcome, we applied an explanation method to generate patient-specific explanations. Three physician reviewers independently evaluated each explanatory feature in the context of the patient's data and were instructed to disagree with a feature if they did not agree with the magnitude of support, the direction of support (supportive versus contradictory), or both. Results The model used for generating predictions achieved a F1 score of 0.43 and area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval [CI]: 0.81–0.87). Interreviewer agreement between two reviewers was strong (Cohen's kappa coefficient = 0.87) and fair to moderate between the third reviewer and others (Cohen's kappa coefficient = 0.49 and 0.33). Agreement rates between reviewers and generated explanations—defined as the proportion of explanatory features with which majority of reviewers agreed—were 0.78 for actual explanations and 0.52 for fabricated explanations, and the difference between the two agreement rates was statistically significant (Chi-square = 19.76, p-value < 0.01). Conclusion There was good agreement among physician reviewers on patient-specific explanations that were generated to augment predictions of clinical outcomes. Such explanations can be useful in interpreting predictions of clinical outcomes.

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.

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