• Publications
  • Influence
Increased risk of COVID‐19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States
TLDR
Individuals with a recent diagnosis of a mental disorder are identified as being at increased risk for COVID‐19 infection, which is further exacerbated among African Americans and women, and as having a higher frequency of some adverse outcomes of the infection. Expand
Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature
TLDR
An automatic learning approach to accurately extract drug-SE pairs from the vast amount of published biomedical literature, a rich knowledge source of side effect information for commercial, experimental, and even failed drugs, shows that the pattern-learning approach is largely complementary to the SVM- and co-occurrence-based approaches with significantly higher precision and F1 but lower recall. Expand
Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing
TLDR
A simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE, showing that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery. Expand
Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection
TLDR
It is demonstrated that large-scale combining information from FAERS and biomedical literature can significantly contribute to drug safety surveillance. Expand
A genome-wide systems analysis reveals strong link between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat
TLDR
This study suggests that TMAO may be an important intermediate marker linking dietary meat and fat and gut microbiota metabolism to risk of CRC, underscoring opportunities for the development of new gut microbiome-dependent diagnostic tests and therapeutics for CRC. Expand
Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature
TLDR
This study presents an automatic approach to extract disease-manifestation (D-M) pairs (one specific type of disease-phenotype relationship) from the wide body of published biomedical literature and creates a large-scale and accurate D-M phenotype relationship knowledge base. Expand
Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System
TLDR
The unique drug-CV association dataset that is created based on FAERS could facilitate the understanding and prediction of cardiotoxic events associated with targeted cancer drugs. Expand
Towards understanding brain-gut-microbiome connections in Alzheimer’s disease
TLDR
This study provides supporting evidence that human gut microbial metabolites may be an important mechanistic link between environmental exposure and various aspects of AD, and provides the foundations for subsequent hypothesis-driven biological and clinical studies of brain-gut-environment interactions in AD. Expand
dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text
TLDR
This study innovates a semi-supervised iterative pattern learning approach that is used to build an precise, large-scale disease-disease risk relationship (D1 →D2) knowledge base (dRiskKB) from a vast corpus of free-text published biomedical literature. Expand
Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis
TLDR
This study demonstrates strong gut-microbiome-immune-joint interactions in RA, which converged on both genetic, functional and phenotypic levels. Expand
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