7 citations · 22 across the 10 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
q-bio.GN2020★ 2 cited
Using ontology embeddings for structural inductive bias in gene expression data analysis
Maja Trębacz, Zohreh Shams, Mateja Jamnik +4
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning. However, such data is extremely highly dimen…
q-bio.MN2020
Incorporating network based protein complex discovery into automated model construction
Paul Scherer, Maja Trȩbacz, Nikola Simidjievski +4
We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs. The s…
cs.LG2020★ 3 cited
Learning distributed representations of graphs with Geo2DR
Paul Scherer, Pietro Lio
We present Geo2DR (Geometric to Distributed Representations), a GPU ready Python library for unsupervised learning on graph-structured data using discrete substructure patterns and…