Exploring the Expression Profiles of Serum Inflammatory Proteins and Potential Antiaging Targets in Chinese Long-Living People.

Chronic inflammation (inflammaging) is one of the important reasons for the development of age-related diseases and aging. Carrying out aging research and mining inflammatory markers can develop antiaging intervention targets, thus promoting healthy aging. By comparing the levels of inflammatory proteome in the serum of Chinese long-living people over 90 years and elderly aged 60∼79 which was detected by Olink platform, this study found that some pro-inflammatory or pro-aging proteins increased significantly in the long-living people, such as c-x-c motif chemokine ligand 9, accompanied by a significant increase in the levels of several anti-inflammatory or antiaging proteins, including fibroblast growth factor 19 and fibroblast growth factor 23, which confirmed that compared with elderly people, pro-inflammatory and anti-inflammatory (pro-aging and antiaging) tend to be balanced in long-living people, thus reducing the risk of age-related diseases and prolonging the lifespan of the elderly.

Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction.

Background: The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier.