Electronics industry is one of the fastest evolving, innovative, and most competitive
industries. In order to meet the high consumption demands on electronics components …

Understanding epigenetic processes holds immense promise for medical applications.
Advances in Machine Learning (ML) are critical to realize this promise. Previous studies …

Extracting biologically meaningful information from chromosomal interactions obtained with
genome-wide chromosome conformation capture (3C) analyses requires the elimination of …

Metalearning attracted considerable interest in the machine learning community in the last
years. Yet, some disagreement remains on what does or what does not constitute a …

Graphs with complete node attributes have been widely explored recently. While in practice,
there is a graph where attributes of only partial nodes could be available and those of the …

Computational integrative analysis has become a significant approach in the data-driven
exploration of biological problems. Many integration methods for cancer subtyping have …

Clustering a graph, ie, assigning its nodes to groups, is an important operation whose best
known application is the discovery of communities in social networks. Graph clustering and …

Betweenness centrality is a fundamental measure in social network analysis, expressing the
importance or influence of individual vertices in a network in terms of the fraction of shortest …

Background: Cardiac resynchronization therapy (CRT) has significant nonresponse rates.
We assessed whether machine learning (ML) could predict CRT response beyond current …

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly
in the current era of smart devices, where enormous data are gathered from numerous …