Several previous studies have suggested that n-3 polyunsaturated fatty acids (n-3 PUFA) can exert favourable effects in patients with heart failure, but the mechanisms involved are not fully understood. This study was designed to investigate the effects of n-3 PUFA on circulating inflammatory markers and N-terminal pro-brain natriuretic peptide (NT-proBNP) in patients with heart failure. Seventy-six patients with heart failure were randomly assigned to receive 2 g/day of n-3 PUFA or placebo for 3 months. Treatment with n-3 PUFA significantly decreased plasma levels of tumour necrosis factor, interleukin-6, intercellular adhesion molecule 1 and NT-proBNP. Left ventricular ejection fraction showed a small, non-significant improvement. High-sensitivity C-reactive protein levels decreased significantly in smokers after n-3 PUFA treatment. Thus, n-3 PUFA can reduce levels of plasma inflammatory markers and NT-proBNP as biomarkers of risk stratification in patients with heart failure. n-3 PUFA may offer a novel therapy for heart failure.

Psoriasis is an autoimmune disease, which symptoms can significantly impair the patient's life quality. It is mainly diagnosed through the visual inspection of the lesion skin by experienced dermatologists. Currently no cure for psoriasis is available due to limited knowledge about its pathogenesis and development mechanisms. Previous studies have profiled hundreds of differentially expressed genes related to psoriasis, however with no robust psoriasis prediction model available. This study integrated the knowledge of three feature selection algorithms that revealed 21 features belonging to 18 genes as candidate markers. The final psoriasis classification model was established using the novel Incremental Feature Selection algorithm that utilizes only 3 features from 2 unique genes, IGFL1 and C10orf99. This model has demonstrated highly stable prediction accuracy (averaged at 99.81%) over three independent validation strategies. The two marker genes, IGFL1 and C10orf99, were revealed as the upstream components of growth signal transduction pathway of psoriatic pathogenesis.

BackgroundHigh-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This “large p, small n” paradigm in the area of biomedical “big data” may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets.ResultsThis work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature.ConclusionMcTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0990-0) contains supplementary material, which is available to authorized users.