Mauricio Soto

The paper replaces non-biomedical synonyms with biomedical terms to improve concept embeddings using Word2Vec, reducing mean intracluster distance by 8%.

miss-SNF integrates multimodal patient data with missing sources by recovering patient similarities and identifying clinically relevant subgroups.

The paper analyzes RNA-KG using homogeneous graph embedding methods to predict node types and edges, achieving high accuracy in classification and edge prediction tasks.

AutoMAxO uses LLMs to semi-automate MAxO curation for rare diseases, identifying 538 confirmed disease-treatment annotations from 4,918 medical abstracts.

The paper introduces SASC, a method for inferring cancer progression from single-cell sequencing data that allows mutation losses, showing high accuracy on simulated and real datasets.

The paper evaluates whether relaxing the infinite sites assumption improves tumor phylogenies using an ILP-based approach, finding that allowing mutation losses provides better interpretations of real and simulated datasets.