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In recent years, with the increase of data scale, multi-label learning with large scale class labels has turned out to be the research hotspots. Due to the huge solution space, the problem becomes more complex. Therefore, we propose a multi-label algorithm based on kernel learning machine in this paper. Besides, the Cholesky matrix decomposition inverse method is adopted to calculate the network output weight of the kernel extreme learning machine. In particular, in terms of large matrix inverse problem, the large matrix is divided into small matrices for parallel computation through using matrix block method. Compared with several state-of -the-art algorithms on several benchmark data sets, results of the experiments show that the proposed algorithm makes a better performance with large scale class labels.

Prior research has indicated that as an important biomarker of chronic low-grade inflammation, high-sensitivity C-reactive protein (hs-CRP) can play important roles on the onset of metabolic syndrome and cardiovascular diseases (CVD). We conducted an integrative approach, which combines biological wet-lab experiments, statistical analysis, and semantics-oriented bioinformatics & computational analysis, to investigate the association among hs-CRP, body fat mass (FM) distribution, and other cardiometabolic risk factors in young healthy women. Research outcomes in this study resulted in two novel discoveries. Discovery 1: There are four primary determinants for hs-CRP, i.e., central/abdominal FM (a.k.a. trunk FM) accumulation, leptin, high density lipoprotein cholesterol (HDL-C), and plasminogen activator inhibitior-1 (PAI-1). Discovery 2: Chronic inflammation may involve in adipocyte-cytokine interaction underlying the metabolic derangement in healthy young women.

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