Biomedical subjects

Daniel McDonald

Publications and source records attributed to Daniel McDonald.

3 recordsLinked to original sources

PubMed2025

Versatile wastewater monitoring of pathogens and antimicrobial resistance enabled by metatranscriptomics and long-read metagenomics.

Widespread interest in the development of population-wide pathogen and antimicrobial resistance (AMR) monitoring has revealed wastewater's microbial footprint as a marker of public health. Near-source wastewater remains a difficult sample type for microbiome analyses but represents a closer link to human health than the downstream products of its treatment. Few studies integrate methods for non-targeted monitoring applications, and critically, current methods cannot connect AMR genes to species, nor resolve full genomes. We address these challenges by developing a pipeline that enables untargeted metagenomics, metatranscriptomics, and novel long-read metagenomics (LRG). We achieve untargeted pathogen detection, limited by highly abundant resident species, while retaining microbial information with near-source sampling. Furthermore, LRG identifies antibiotic resistance gene-containing microbes and enables assembly of culture-independent genomes with previously unreported AMR genes. We establish an integrated approach to broadly monitor pathogens in wastewater, while demonstrating the importance of LRG to illuminate microbial AMR at the species level.

Journal Article↗

PubMed2006

Aggregating automatically extracted regulatory pathway relations.

Automatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extracted relations. Organizational processes should consolidate multiple references to the same objects over various levels of granularity, connect those references to other resources, and capture contextual information. We propose a feature decomposition approach to relation aggregation to support a five-level aggregation framework. Our BioAggregate tagger uses this approach to identify key features in extracted relation name strings. We show encouraging feature assignment accuracy and report substantial consolidation in a network of extracted relations.

Artificial Intelligence↗

PubMed2005

Linking ontological resources using aggregatable substance identifiers to organize extracted relations.

Systems that extract biological regulatory pathway relations from free-text sources are intended to help researchers leverage vast and growing collections of research literature. Several systems to extract such relations have been developed but little work has focused on how those relations can be usefully organized (aggregated) to support visualization systems or analysis algorithms. Ontological resources that enumerate name strings for different types of biomedical objects should play a key role in the organization process. In this paper we delineate five potentially useful levels of relational granularity and propose the use of aggregatable substance identifiers to help reduce lexical ambiguity. An aggregatable substance identifier applies to a gene and its products. We merged 4 extensive lexicons and compared the extracted strings to the text of five million MEDLINE abstracts. We report on the ambiguity within and between name strings and common English words. Our results show an 89% reduction in ambiguity for the extracted human substance name strings when using an aggregatable substance approach.

Algorithms↗