Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed a multi-omics data-affinitive artificial intelligence algorithm based on the graph convolutional network that integrates mRNA expression, DNA methylation, and DNA sequencing data. This NSCLC prediction model achieved a 93.7% macro F1-score, indicating that values for false positives and negatives were substantially low, which is desirable for accurate classification. Gene ontology enrichment and pathway analysis of features revealed that two major subtypes of NSCLC, lung adenocarcinoma and lung squamous cell carcinoma, have both specific and common GO biological processes. Numerous biomarkers (i.e., microRNA, long non-coding RNA, differentially methylated regions) were newly identified, whereas some biomarkers were consistent with previous findings in NSCLC (e.g., SPRR1B). Thus, using multi-omics data integration, we developed a promising cancer prediction algorithm.

Recently there has been a trend in bioinformatics to produce and manage large quantities of data to better explain complex life phenomena through relationship and interactions among biomedical entities. This increase in data leads to a need for more efficient management and searching capabilities. As a result, Semantic Web technologies have been applied to biomedical data. To use these technologies, users have to learn a query language such as SPARQL in order to ask complex queries such as ‘What are the drugs associated with the disease breast carcinoma and Osteoporosis but not the gene ESR1’. BEE was developed to overcome the limitations and difficulties of learning such query languages. Our proposed system provides an intuitive and effective query interface based on natural language. Our system is a heterogeneous biomedical entity query system based on pathway, drug, microRNA, disease and gene datasets from DGIdb, Tarbase, Human Phenotype Ontology and Reactome, Gene Ontology, KEGG gene set of MSigDB. User queries can be joined with union, intersection and negation operators. The system also allows for selected results to be saved and later combined with newly created queries. To the best of our knowledge, BEE is the first system that supports condition search based on the relationship of heterogeneous biomedical entities and is expected to be used in various fields of bioinformatics such as in drug repositioning candidate selection as well as simple knowledge search.