With the exponential growth of biomedical literature, leveraging Large Language Models (LLMs) for automated medical knowledge understanding has become increasingly critical for advancing precision medicine. However, current approaches face significant challenges in reliability, verifiability, and scalability when extracting complex biological relationships from scientific literature using LLMs. To overcome the obstacles of LLM development in biomedical literature understating, we propose LORE, a novel unsupervised two-stage reading methodology with LLM that models literature as a knowledge graph of verifiable factual statements and, in turn, as semantic embeddings in Euclidean space. LORE captured essential gene pathogenicity information when applied to PubMed abstracts for large-scale understanding of disease-gene relationships. We demonstrated that modeling a latent pathogenic flow in the semantic embedding with supervision from the ClinVar database led to a 90% mean average precision in identifying relevant genes across 2097 diseases. This work provides a scalable and reproducible approach for leveraging LLMs in biomedical literature analysis, offering new opportunities for researchers to identify therapeutic targets efficiently.

SUMMARY: In higher eukaryotes, the generation of transcript isoforms from a single gene through alternative splicing (AS) and alternative transcription (AT) mechanisms increases functional and regulatory diversities. Annotating these alternative transcript events is essential for genomic studies. However, there are no existing tools that generate comprehensive annotations of all these alternative transcript events including both AS and AT events. In the present study, we develop CATANA, with the encoded exon usage patterns based on the flattened gene model, to identify ten types of AS and AT events. We demonstrate the power and versatility of CATANA by showing greater depth of annotations of alternative transcript events according to either genome annotation or RNA-seq data. AVAILABILITY AND IMPLEMENTATION: CATANA is available on https://github.com/shiauck/CATANA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.