Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data

Metabolomics data are complex with a high degree of multicollinearity. As such, multivariate linear projection methods, such as partial least squares discriminant analysis (PLS-DA) have become standard. Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more appropriate to model potential nonlinear latent covariance; however, they are not widely used due to difficulty in deriving statistical inference, and thus biological interpretation. Herein, we illustrate the utility of ANNs for clinical metabolomics using publicly available data sets and develop an open framework for deriving and visualising statistical inference from ANNs equivalent to standard PLS-DA methods

Untargeted metabolomics of childhood asthma exacerbations from retrospectively collected serum samples

Asthma is a common chronic inflammatory disease of the airways, affecting 360 million people globally. It is characterised by chronic inflammation, recurrent episodes of bronchoconstriction and mucosal hypersecretion. Symptoms include chest tightness, shortness of breath, coughing and wheezing. Oral corticosteroids used for the treatment of asthma have adverse effects, including bone mineralisation and adrenal suppression. Not all children with acute asthma-like symptoms will have persistent asthma in the future. This is particularly common at a pre-school age. This persistence is only known retrospectively. Identifying children early in their disease course can better direct treatment. Additionally, further understanding of the underlying disease mechanisms can direct novel therapies. Metabolomics is the systematic study of metabolites in a biological system. Metabolites are low molecular weight biochemical that are reactants, intermediates and end products of biological reactions. They are highly sensitive and are a snapshot of a particular biochemistry and/or pathophysiology. However, they are also highly sensitive to analytical variation. Thus, there were three aims in this study: to assess the impact of potentially limiting factors of retrospectively collected serum samples on metabolomic analysis; to determine whether metabolomics can identify potential biomarkers to distinguish wheeze/asthma exacerbation and control groups; and to determine whether metabolomics-derived biomarkers can identify differences between preschool-aged and school-aged phenotypes. Serum samples were curated from the Mechanisms of Acute Viral Respiratory Infections in Children (MAVRIC) study. This cohort study recruited children upon presentation to the emergency department at Princess Margaret Hospital with acute lower respiratory illnesses including wheeze/asthma. One–hundred and sixty-one samples were from children with acute wheeze/asthma, and 51 were from healthy controls. Samples were previously stored between 0.8 to 7.9 years at −80 °C. Samples were extracted, derivatised and subsequently analysed using GC-QTOF-MS. SpectralWorks’ AnalyzerPro® was used for the deconvolution and untargeted processing of the metabolite data. The quality control-robust spline correction (QC-RSC) algorithm was used for inter- and intra- batch correction. Putative identification of metabolites was made using the NIST (v2.0) library. Fifty-two metabolites were included in the data analysis, with 24 putatively identified metabolites. The effects of storage time on metabolites were determined via Spearman’s correlation. There was a significant difference (p-value < 0.05) in metabolite abundances for succinate, serine and tryptophan; however, the correlation was weak. A two-way Analysis of Variance was performed to compare acute wheeze/asthma vs. healthy, pre-school vs. school age and the associated interactions. Twenty-nine metabolites were found to be significantly different (p-value < 0.05) between the acute wheeze/asthma and the control group. The sub-classes of metabolites include amino acids, fatty acids and sugars. Principal Component Analysis showed a difference in spread between the acute wheeze/asthma and control group. However, there was no clear difference between preschool and school-aged brackets for each group. Arabinofuranose and creatinine were significantly different (p-value < 0.05) between pre-school and school-aged subjects with acute wheeze/asthma. Creatinine is potentially being indicative of higher ASM stress and damage in the school-aged subjects with acute wheeze/asthma

Methodologies for evaluating the playability of mobile games:systematic literature review

Tiivistelmä. The gaming industry has been growing rapidly during the past years due to the interest of the new generations in mobile gaming. To deliver a great experience for the gamers, it is required for the gaming companies to produce games that are challenging but at the same time easy to play. To achieve this, it is required to understand the factors that affect the gaming experience. Playability is a term that is used to understand the usability of a game and its experience. The purpose of this thesis was to understand what is known related to the playability of mobile games and to identify the methodologies that are used by the community to evaluate this phenomenon. To find the answers to these questions, it was performed a systematic literature review (SLR) using the databases Scopus, IEEE Xplore, and Web of Science. After conducting the SLR, 1,390 studies related to the playability of mobile games were found from which 27 were identified as primary studies of this research. From the data collected from the primary studies, there were identified 12 different methodologies that are used for evaluating the playability of mobile games. The methodologies that are most suitable to assess the playability of mobile games are heuristic evaluation and playtesting. Other methodologies can be used for evaluating the playability of mobile games, but they must include a set of heuristics that allows evaluating the playability. The limitations of the research were mentioned, and it was proposed topics for future research of this field. The contribution of this thesis is the summarizing of the current methodologies that are used to understand and evaluate the playability of mobile games. The results of this thesis are valuable for game developers, game designers, and game usability practitioners

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification

Introduction: Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models. Objectives: We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis. Methods: We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks. Results: There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice. Conclusion: The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm

Migrating from partial least squares discriminant analysis to artificial neural networks: A comparison of functionally equivalent visualisation and feature contribution tools using Jupyter Notebooks

Introduction: Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods. Objectives: We hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN. Methods: We compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub. Results: The migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach. Conclusion: We have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures

Liderazgo transformacional y comportamiento organizacional de los colaboradores en la Municipalidad provincial de Huanta, Ayacucho 2022

El presente trabajo de investigación titulado “liderazgo transformacional y comportamiento organizacional de los Colaboradores en la Municipalidad Provincial De Huanta, Ayacucho 2022”, cuyo objetivo general fue analizar la relación que existe entre el liderazgo transformacional y el comportamiento organizacional de los colaboradores en la Municipalidad Provincial de Huanta, Ayacucho 2022. La investigación fue de tipo aplicada, enfoque cuantitativo, el nivel del estudio fue correlacional, además de corte transversal y el diseño no experimental, asimismo de población finita compuesta por 292 colaboradores donde se consideró como muestra a 167 colaboradores. La encuesta fue la técnica empleada en la investigación, además del instrumento para la recopilación de los datos fue el cuestionario y con una escala tipo Likert, la cual se le dio validación mediante el alfa de Cronbach arrojando como resultado 0,782 que significa que la confiabilidad es marcada. Se aplicó estadística en la cual se hizo el procesamiento de información recopilada, finalmente se concluyó que existe una correlación positiva alta del liderazgo transformacional y el comportamiento organizacional de los colaboradores en la Municipalidad Provincial de Huanta, Ayacucho 2022, con un coeficiente de correlación Spearman = 0,728

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

Background A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. Aim of Review To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. Key Scientific Concepts of Review This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform