Association of intrinsic capacity with incidence and mortality of cardiovascular disease: Prospective study in UK Biobank.

Background: The World Health Organization proposed the concept of intrinsic capacity (IC; the composite of all the physical and mental capacities of the individual) as central for healthy ageing. However, little research has investigated the interaction and joint associations of IC with cardiovascular disease (CVD) incidence and CVD mortality in middle- and older-aged adults.

Methods: Using data from 443 130 UK Biobank participants, we analysed seven biomarkers capturing the level of functioning of five domains of IC to calculate a total IC score (ranging from 0 [better IC] to +4 points [poor IC]).

Association of intrinsic capacity with respiratory disease mortality.

The World Health Organization (WHO) introduced a framework for healthy aging in 2015 that emphasizes functional ability instead of absence of disease. Healthy ageing is defined as "the process of building and maintaining the functional ability that enables well-being". This framework considers an individual's intrinsic capacity (IC), environment, and the interaction between them to determine functional ability.

Dealing with risk discontinuities to estimate cancer mortality risks when the number of small areas is large.

Many statistical models have been developed during the last years to smooth risks in disease mapping. However, most of these modeling approaches do not take possible local discontinuities into consideration or if they do, they are computationally prohibitive or simply do not work when the number of small areas is large. In this paper, we propose a two-step method to deal with discontinuities and to smooth noisy risks in small areas.

Inference of population structure using genetic markers and a Bayesian model averaging approach for clustering.

The analysis of the structure of populations on the basis of genetic data is essential in population genetics. It is used, for instance, to study the evolution of species or to correct for population stratification in association studies. These genetic data, normally based on DNA polymorphisms, may contain irrelevant information that biases the inference of population structure.

Bayesian model averaging of naive Bayes for clustering.

This paper considers a Bayesian model-averaging (MA) approach to learn an unsupervised naive Bayes classification model. By using the expectation model-averaging (EMA) algorithm, which is proposed in this paper, a unique naive Bayes model that approximates an MA over selective naive Bayes structures is obtained. This algorithm allows to obtain the parameters for the approximate MA clustering model in the same time complexity needed to learn the maximum-likelihood model with the expectation-maximization algorithm.

Machine learning in bioinformatics.

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.