< em > gene2gauss < /em > : A multi-view gaussian gene embedding learner for analyzing transcriptomic networks

We present gene2gauss, a novel feature learning framework that is capable of embedding genes as multivariate gaussian distributions by taking into account their long-range interaction neighborhoods across multiple transcriptomic studies. Using multiple gene co-expression networks from idiopathic pulmonary fibrosis, we demonstrate that these multi-dimensional gaussian features are suitable for identifying regulons of known transcription factors (TF). Using standard TF-target libraries, we demonstrate that the features from our method are highly relevant in comparison with other feature learning approaches on transcriptomic data.PMID:35854722 | PMC:PMC9285176

Source: AMIA Annual Symposium Proceedings - July 20, 2022 Category: Bioinformatics Authors: Sudhir Ghandikota Anil G Jegga Source Type: research