Background Cancer is a complex disease. Fundamental cellular based studies as well as
modeling provides insight into cancer biology and strategies to treatment of the disease. In …

Graph representation learning is a method for introducing how to effectively construct and
learn patient embeddings using electronic medical records. Adapting the integration will …

Abstract Background When modeling in Systems Biology and Systems Medicine, the data is
often extensive, complex and heterogeneous. Graphs are a natural way of representing …

The development of colorectal cancer (CRC)—the third most common cancer type—has
been associated with deregulations of cellular mechanisms stimulated by both genetic and …

There are many steps in analyzing transcriptome data, from the acquisition of raw data to the
selection of a subset of representative genes that explain a scientific hypothesis. The data …

Over the last two decades, the importance of epigenetics for interpretation of diverse genetic
and genomic data has become increasingly clear. The need for study of indirect (non-gene) …

Interoperability is the faculty of making information systems work together. In this paper we
will distinguish a number of different forms that interoperability can take and show how they …

While using data standards can facilitate research by making it easier to share data,
manually map** to data standards creates an obstacle to their adoption. Semi-automated …

GeNNet: An Integrated platform for unifying scientific workflow management and graph
databases for transcriptome data analysis T Page 1 GeNNet: An Integrated platform for unifying …

Heterogeneous graphs are a data format for graphs that could define complicated and
diverse real-world interactions by accommodating distinct sorts of nodes and edge types …