Machine Learning Approaches for the Prediction of Obesity using Publicly Available Genetic Profiles

This paper presents a novel approach based on the analysis of genetic variants from publicly available genetic profiles and the manually curated database, the National Human Genome Research Institute Catalog. Using data science techniques, genetic variants are identified in the collected participant profiles then indexed as risk variants in the National Human Genome Research Institute Catalog. Indexed genetic variants or Single Nucleotide Polymorphisms are used as inputs in various machine learning algorithms for the prediction of obesity. Body mass index status of participants is divided into two classes, Normal Class and Risk Class. Dimensionality reduction tasks are performed to generate a set of principal variables - 13 SNPs - for the application of various machine learning methods. The models are evaluated using receiver operator characteristic curves and the area under the curve. Machine learning techniques including gradient boosting, generalized linear model, classification and regression trees, K-nearest neighbours, support vector machines, random forest and multilayer neural network are comparatively assessed in terms of their ability to identify the most important factors among the initial 6622 variables describing genetic variants, age and gender, to classify a subject into one of the body mass index related classes defined in this study. Our simulation results indicated that support vector machine generated high accuracy value of 90.5%

Can universe exit from phantom inflation due to gravitational back reaction?

The effects of the gravitational back reaction of cosmological perturbations are investigated in a phantom inflation model. The effective energy-momentum tensor of the gravitational back reaction of cosmological perturbations whose wavelengths are larger than the Hubble radius is calculated. Our results show that the effects of gravitational back reaction will counteract that of the phantom energy. It is demonstrated in a chaotic phantom inflation model that if the phantom field at the end of inflation is larger than a critical value determined by the necessary e-folds, the phantom inflation phase might be terminated by the gravitational back reaction.Comment: 9 pages, Revtex4, to appear in JCA

Plus ça change : pots, crucibles and the development of metallurgy in Chalcolithic Las Pilas (Mojácar, Spain)

This paper considers the structure of production, distribution and consumption of ceramics within Chalcolithic communities of SE Iberia, an important region for modelling social and technological change in the recent prehistory of Eurasia. Our research provides new data through the comparative analysis of domestic and metallurgical ceramics, as well as building and other clay-rich materials from the archaeological site of Las Pilas (2875–2620 cal. BC 2σ to 2460–2205 cal. BC 2σ) (Mojácar, Almería). In total, 56 samples are characterised by optical petrography, with SEM analysis of 22 of those individuals, in order to assess firing conditions. Results point to the existence of a local tradition in which domestic and metallurgical wares exhibit important similarities in their production processes. In terms of technology, the assemblage shows a relative homogeneity, although firing conditions, surface treatment and decoration seem to have played an important role in the differentiation of highly symbolic wares from other ceramics. We conclude that raw material procurement and processing at Las Pilas differ from those at other Copper Age sites already studied in SE and SW Iberia. This is in agreement with earlier archaeometallurgical studies on Las Pilas, suggesting the development of local and community-based technological traditions. As such, the paper attempts to bridge the recent divide between re-emergent top-down models and our detailed understandings of technological practice

A Genetic Analytics Approach for Risk Variant Identification to Support Intervention Strategies for People Susceptible to Polygenic Obesity and Overweigh

Obesity is a growing epidemic that has increased steadily over the past several decades. It affects significant parts of the global population and this has resulted in obesity being high on the political agenda in many countries. It represents one of the most difficult clinical and public health challenges worldwide. While eating healthy and exercising regularly are obvious ways to combat obesity, there is a need to understand the underlying genetic constructs and pathways that lead to the manifestation of obesity and their susceptibility metrics in specific individuals. In particular, the interpretation of genetic profiles will allow for the identification of Deoxyribonucleic Acid variations, known as Single Nucleotide Polymorphism, associated with traits directly linked to obesity and validated with Genome-Wide Association Studies. Using a robust data science methodology, this paper uses a subset of the TwinsUK dataset that contains genetic data from extremely obese individuals with a BMI≥40, to identify significant obesity traits for potential use in genetic screening for disease risk prediction. The approach posits a framework for methodical risk variant identification to support intervention strategies that will help mitigate long-term adverse health outcomes in people susceptible to obesity and overweight

Association Mapping Approach into Type 2 Diabetes using Biomarkers and Clinical Data

The global growth in incidence of Type 2 Diabetes (T2D) has become a major international health concern. As such, understanding the aetiology of Type 2 Diabetes is vital. This paper investigates a variety of statistical method-ologies at various level of complexity to analyse genotype data and identify bi-omarkers that show evidence of increase susceptibility to T2D and related traits. A critical overview of several selected statistical methods for population-based association mapping particularly case-control genetic association analysis is pre-sented. A discussion on a dataset accessed in this paper that includes 3435 female subjects for cases and controls with genotype information across 879071 Single Nucleotide Polymorphism (SNPs) is presented. Quality control steps into the dataset through pre-processing phase are performed to remove samples and markers that failed the quality control test. Association analysis is discussed to address which statistical method can be appropriate to the dataset. Our genetic association analysis produces promising results and indicated that Allelic asso-ciation test showed one SNP above the genome-wide significance threshold of 5×10−8 which is rs10519107 (Odds Ratio (OR)=0.7409,P−Value (P)=1.813×10−9), While, there are several SNPs above the suggestive association threshold of 5×10−6 these SNPs could worth further investigation. Furthermore, Logistic Regression analysis adjusted for multiple confounder factors indicated that none of the genotyped SNPs has passed genome-wide significance threshold of 5×10−8 . Nevertheless, four SNPs (rs10519107, rs4368343, rs6848779, rs11729955) have passed suggestive association threshold

New Isotropic and Anisotropic Sudden Singularities

We show the existence of an infinite family of finite-time singularities in isotropically expanding universes which obey the weak, strong, and dominant energy conditions. We show what new type of energy condition is needed to exclude them ab initio. We also determine the conditions under which finite-time future singularities can arise in a wide class of anisotropic cosmological models. New types of finite-time singularity are possible which are characterised by divergences in the time-rate of change of the anisotropic-pressure tensor. We investigate the conditions for the formation of finite-time singularities in a Bianchi type $VII_{0}$ universe with anisotropic pressures and construct specific examples of anisotropic sudden singularities in these universes.Comment: Typos corrected. Published versio

On the Past Asymptotic Dynamics of Non-minimally Coupled Dark Energy

We apply dynamical systems techniques to investigate cosmological models inspired in scalar-tensor theories written in the Einstein frame. We prove that if the potential and the coupling function are sufficiently smooth functions, the scalar field almost always diverges into the past. The dynamics of two important invariant sets is investigated in some detail. By assuming some regularity conditions for the potential and for the coupling function, it is constructed a dynamical system well suited to investigate the dynamics where the scalar field diverges, i.e. near the initial singularity. The critical points therein are investigated and the cosmological solutions associated to them are characterized. We find that our system admits scaling solutions. Some examples are taken from the bibliography to illustrate the major results. Also we present asymptotic expansions for the cosmological solutions near the initial space-time singularity, which extend in a way previous results of other researchers.Comment: 38 pages, 2 figures, accepted for publication in CQ

Evaluation of Phenotype Classification Methods for Obesity using Direct to Consumer Genetic Data

Today, Direct-to-Consumer genetic testing services are becoming more ubiquitous. Consumers of such services are sharing their genetic and clinical information with the research community to facilitate the extraction of knowledge about different conditions. In this paper, we build on these services to analyse the genetic data of people with different BMI levels to determine the immediate and long-term risk factors associated with obesity. Using web scraping techniques, a dataset containing publicly available information about 230 participants from the Personal Genome Project is created. Subsequent analysis of the dataset is conducted for the identification of genetic variants associated with high BMI levels via standard quality control and association analysis protocols for Genome Wide Association Analysis. Finally, we applied a combination of Recursive Feature Elimination feature selection and Support Vector Machine with Radial Basis Function Kernel learning method to the filtered dataset. Using a robust data science methodology our approach provides the identification of obesity related genetic variants, to be used as features when predicting individual obesity susceptibility. The results reveal that the subset of features obtained through Recursive Feature Elimination does not improve the performance of the classifier when compared to the totality of genetic variants identified in logistic regression

An Ensemble Detection Model using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities

Fuel poverty has a negative impact on the wellbeing of individuals within a household; affecting not only comfort levels but also increased levels of seasonal mortality. Wellbeing solutions within this sector are moving towards identifying how the needs of people in vulnerable situations can be improved or monitored by means of existing supply networks and public institutions. Therefore, the focus of this research is towards wellbeing monitoring solution, through the analysis of gas smart meter data. Gas smart meters replace the traditional analogue electro-mechanical and diaphragm-based meters that required regular reading. They have received widespread popularity over the last 10 years. This is primarily due to the fact that by using this technology, customers are able to adapt their consumption behaviours based on real-time information provided by In-Home Devices. Yet, the granular nature of the datasets generated has also meant that this technology is ideal for further scalable wellbeing monitoring applications. For example, the autonomous detection of households at risk of energy poverty is possible and of growing importance in order to face up to the impacts of fuel poverty, quality of life and wellbeing of low-income housing. However, despite their popularity (smart meters), the analysis of gas smart meter data has been neglected. In this paper, an ensemble model is proposed to achieve autonomous detection, supported by four key measures from gas usage patterns, consisting of i) a tariff detection, ii) a temporally-aware tariff detection, iii) a routine consumption detection and iv) an age-group detection. Using a cloud-based machine learning platform, the proposed approach yielded promising classification results of up to 84.1% Area Under Curve (AUC), when the Synthetic Minority Over-sampling Technique (SMOTE) was utilised