Biology subjects

Ho, Y.-L.

Publications and source records attributed to Ho, Y.-L..

3 recordsLinked to original sources

bioRxiv2026

Detecting Manuscripts Related to Computable Phenotypes Using a Transformer-based Language Model

ObjectiveThe demand for a comprehensive phenomics library, which requires identifying computable phenotype definitions and associated metadata from an ever-expanding biomedical literature, presents a significant, labor-intensive, and unscalable challenge. To address this, we introduce a transformer-based language model specifically designed for identifying biomedical texts containing computable phenotypes and piloted its use in the Centralized Interactive Phenomics Resource (CIPHER) platform. Materials and MethodsWe fine-tuned a BioBERT model using a labeled dataset of 396 manuscripts. The model incorporates our novel sliding-window approach to effectively overcome token-length limitations, thereby enabling accurate classification of full-length manuscripts. For scalable deployment and continuous refinement, we developed a cohesive framework that integrates a web-based user interface, a control server, and a classification module. ResultsThe staged approach for model development yielded a final model with 95% accuracy. The web-based user interface was deployed on the CIPHER platform and enables user feedback for model retraining. DiscussionWe developed a model and user interface which are currently in use by data curators to identify computable phenotype definitions from the literature. ConclusionThrough this system, users can submit literature, assess classification results, and provide feedback directly influencing future model training, thereby offering an efficient and adaptive solution for accelerating phenotype-driven literature curation.

bioinformatics↗

bioRxiv2025

Mesenteric ischemia and bacterial translocation precipitate the intoxication phase of yellow fever

Yellow fever (YF) is classically conceptualized as a hepatotropic disease; indeed, the liver is the primary site of yellow fever virus (YFV) replication. However, circumstantial evidence suggests that extra-hepatic disease may be important for the [~]30% of YF cases that progress to the severe "intoxication" phase of the disease. Using a Syrian hamster-adapted (HA)-YFV, we worked backwards from observations in humans to examine early events that precipitate the intoxication phase of YF. HA-YFV caused severe disease in [~]80% of infected animals characterized by lethargy and weight loss that progressed to widespread petechiae and death by day 6. Clinical chemistry, coagulation testing, histology, immunohistochemistry, and in-situ hybridization were consistent with a cascade of hepatocyte-specific virus replication causing liver damage and a defect in clotting factor synthesis. Despite a lack of extra-hepatic HA-YFV replication, severe pathology was observed in the intestines and pancreas. Histopathological analysis over the time-course of HA-YFV infection revealed an ischemic pattern in these tissues, culminating in fibrinoid/coagulative necrosis of these organs. Further investigation showed that ischemia-induced erosion of the gut epithelial barrier serves as an entry point for luminal bacteria that spread systemically via the portal system. Thus, the intoxication phase of YF is a sepsis-like syndrome caused by translocation of bacteria from a damaged gastrointestinal tract. Evaluation of human YF cases for these previously overlooked disease features confirmed this overarching mechanism: bacteria were identified in the portal vein and liver parenchyma of fatal YF cases along with elevations in plasma markers of bacteremia and a bacteria-driven inflammatory response. Importantly, blood concentrations of the gastrointestinal damage marker intestinal fatty acid binding protein (I-FABP) were significantly elevated in fatal YF cases relative to non-fatal cases, suggesting that I-FABP measurements could be useful in prognosis and treatment decision making. Our findings tie together several recent and historically unexplained observations surrounding the highly-lethal intoxication phase of YF in humans: a high AST/ALT ratio, "black vomit," pancreatitis, and paradoxical neutrophilia. A better appreciation for the drivers of mesenteric ischemia, and preemption of bacterial sepsis, may improve outcomes in cases of severe YF.

microbiology↗

bioRxiv2019

High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS

ObjectiveElectronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP).\n\nMethodWe developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the UMLS. Aggregated ICD and NLP counts along with healthcare utilization were jointly analyzed by fitting an ensemble of latent mixture models. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort PheWAS for two SNPs with known associations.\n\nResultsThe MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes.\n\nConclusionThe MAP approach increased the accuracy of phenotype definition while maintaining scalability, facilitating use in studies requiring large scale phenotyping, such as PheWAS.

bioinformatics↗