Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

Abstract

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical embeddings derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., ‘drug treats disease’, ‘gene interacts with gene’). We show how Bioteque embeddings facilitate the assessment of high-throughput interactome data, pharmacogenomics and drug repurposing exercises, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

Prioritization of new candidate genes for rare genetic diseases by a disease-aware evaluation of heterogeneous molecular networks

“…Thus, RD diagnosis can become a “needle in a haystack” task where the solution is still far connected to current knowledge. In consequence, to increase their efficiency in difficult cases, gene-disease prediction methods are encouraged to use new and various types of functional annotations and combine them accurately (15, 16, 61, 62). In this study, we first confirmed that different types of gene-gene functional association networks had different capabilities in recovering known genes associated with a large collection of RDs.…”

“…Gene annotations not represented as gene or protein relationships were transformed into gene-gene co-annotation networks, describing genes (nodes) and their functional relationship (edges). Thus, for networks describing human gene-phenotype similarity using Human Phenotype Ontology (HPO) terms (32) and phenotype similarity using mouse orthologs from the Mouse Genome Informatics (MGI) (16) we calculated Jaccard similarity for each pair of genes sharing at least one HPO term and constructed a null distribution of Jaccard values to compute z-scores. Significant gene interactions (z-score>1.96; p-values<0.05) were selected to generate the network of phenotypes.…”

“…Alternative strategies were run across 63 PanelApp GAG gene sets and validated in RG genes. Recall-at-k was measured to quantify what fraction of all the disease genes are ranked within the first k predicted genes (k = 8, 16,32,64,128,256,512). The best integration methods are selected at recall-at-n (validation set size).…”

“…They have in common the ability to screen large datasets to extract hidden associations. These sources can be of different nature, ranging from literature to different flavors of omics data, including their integration (5, 7) which has been reported to increase accuracy (15, 16). On top of this, resources such as PanelApp (17) provide manually curated candidate genes for genetic diseases based on community feedback.…”