Biological networks constructed from varied data can be used to map cellular function, but
each data type has limitations. Network integration promises to address these limitations by
combining and automatically weighting input information to obtain a more accurate and
comprehensive representation of the underlying biology. We developed a deep learning-
based network integration algorithm that incorporates a graph convolutional network
framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns …