… Xu Lei1 and Yu Lei contribute equally in writing and editing this review. Jin-Ke Li, Wei-Xing
Du, Ru-Gui Li, Jing Yang and Jian Li participated in data organization and discussion. …

Feature selection is applied to reduce the number of features in many applications where
data has hundreds or thousands of features. Existing feature selection methods mainly focus …

Feature selection, as a preprocessing step to machine learning, is effective in reducing
dimensionality, removing irrelevant data, increasing learning accuracy, and improving result …

Eleven susceptibility loci for late-onset Alzheimer's disease (LOAD) were identified by previous
studies; however, a large portion of the genetic risk for this disease remains unexplained. …

This paper introduces concepts and algorithms of feature selection, surveys existing feature
selection algorithms for classification and clustering, groups and compares different …

NLR (nucleotide-binding domain, leucine-rich repeat–containing) proteins have rapidly
emerged as central regulators of immunity and inflammation with demonstrated relevance to …

Recommender systems are used to accurately and actively provide users with potentially
interesting information or services. Collaborative filtering is a widely adopted approach to …

The detection of explosives is one of the current pressing concerns in global security. In the
past few decades, a large number of emissive sensing materials have been developed for …

Nutritional metal ions play critical roles in many important immune processes. Hence, the
effective modulation of metal ions may open up new forms of immunotherapy, termed as …

The lithium-oxygen battery, of much interest because of its very high-energy density,
presents many challenges, one of which is a high-charge overpotential that results in large …