The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML methodologies on CVD prediction, especially compared to established risk tool, the HellenicSCORE. Data from the ATTICA prospective study (n = 2020 adults), enrolled during 2001–02 and followed-up in 2011–12 were used. Three different machine-learning classifiers (k-NN, random forest, and decision tree) were trained and evaluated against 10-year CVD incidence, in comparison with the HellenicSCORE tool (a calibration of the ESC SCORE). Training datasets, consisting from 16 variables to only 5 variables, were chosen, with or without bootstrapping, in an attempt to achieve the best overall performance for the machine learning classifiers. Depending on the classifier and the training dataset the outcome varied in efficiency but was comparable between the two methodological approaches. In particular, the HellenicSCORE showed accuracy 85%, specificity 20%, sensitivity 97%, positive predictive value 87%, and negative predictive value 58%, whereas for the machine learning methodologies, accuracy ranged from 65 to 84%, specificity from 46 to 56%, sensitivity from 67 to 89%, positive predictive value from 89 to 91%, and negative predictive value from 24 to 45%; random forest gave the best results, while the k-NN gave the poorest results. The alternative approach of machine learning classification produced results comparable to that of risk prediction scores and, thus, it can be used as a method of CVD prediction, taking into consideration the advantages that machine learning methodologies may offer.

Statistical methods are usually applied in examining diet–disease associations, whereas factor analysis is commonly used for dietary pattern recognition. Recently, machine learning (ML) has been also proposed as an alternative technique in health classification. In this work, the predictive accuracy of statistical v. ML methodologies as regards the association of dietary patterns on CVD risk was tested. During 2001–2002, 3042 men and women (45 (sd 14) years) were enrolled in the ATTICA study. In 2011–2012, the 10-year CVD follow-up was performed among 2020 participants. Item Response Theory was applied to create a metric of combined 10-year cardiometabolic risk, the ‘Cardiometabolic Health Score’, that incorporated incidence of CVD, diabetes, hypertension and hypercholesterolaemia. Factor analysis was performed to extract dietary patterns, on the basis of either foods or nutrients consumed; linear regression analysis was used to assess their association with the cardiometabolic score. Two ML techniques (k-nearest-neighbor’s algorithm and random-forests decision tree) were applied to evaluate participants’ health based on dietary information. Factor analysis revealed five and three factors from foods and nutrients, respectively, explaining 54 and 65 % of the total variation in intake. Nutrient and food pattern regression models showed similar accuracy in correctly classifying an individual according to the cardiometabolic risk (R²=9·6 % and R²=8·3 %, respectively). ML techniques were superior compared with linear regression in correct classification of the individuals according to the Health Score (accuracy approximately 38 v. 6 %, respectively), whereas the two ML methods showed equal classification ability. Conclusively, ML methods could be a valuable tool in the field of nutritional epidemiology, leading to more accurate disease-risk evaluation.

Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it’s sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They’re often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all.

BACKGROUND:Studies on the effects of sociodemographic factors on health in aging now include the use of statistical models and machine learning. The aim of this study was to evaluate the determinants of health in aging using machine learning methods ...

Approximately 10% of breast cancer (BC) cases are hereditary (HBC), with HBC most commonly encountered in the context of hereditary breast and ovarian cancer syndrome (HBOC). Although thousands of loss-of-function (LoF) alleles in over 20 genes have been associated with HBC susceptibility, the genetic etiology of approximately 70% of cases remains unexplained. We focused on one of the least-studied European populations and applied whole exome sequencing (WES) to 52 individuals from 17 Greek HBOC families, in which at least one patient was negative for known HBC risk mutations. Initial screening revealed mutations in known cancer genes, including BARD1:p.Trp91* detected in a cancer-free individual, and MEN1:p.Glu260Lys detected in a BC patient. Gene and variant-based approaches were applied to exome data to identify candidate risk variants outside of known risk genes. Findings were verified in a collection of Canadian HBOC patients of European ancestry (FBRCAX), in an independent group of Canadian BC patients and controls, as well as in individuals from The Cancer Genome Atlas (TCGA) and the UK Biobank (UKB). Rare LoF variants were uncovered in MDM1 and NBEAL1 in Greek and Canadian HBOC patients. We also report prioritized missense variants SETBP1:c.4129G>C and C7orf34:c.248C>T. These variants comprise promising candidates whose role in cancer pathogenicity needs to be explored further.