Global trends of antimicrobial resistance and virulence of Klebsiella pneumoniae from different host sources.

Background: Klebsiella pneumoniae is ubiquitous in animals, humans, and the environment, facilitating the dissemination of antimicrobial resistance (AMR) and virulence traits. Most studies are primarily focused on human clinical isolates, leaving critical gaps in understanding non-human reservoirs and cross-species transmission risks.

Methods: We combined large-scale genomic analyses with in vitro and in vivo infection models to characterize the evolutionary dynamics of 2809 K.

Discovery of viruses and bacteria associated with swine respiratory disease on farms at a nationwide scale in China using metatranscriptomic and metagenomic sequencing.

Respiratory disease (RD) is a worldwide leading threat to the pig industry, but there is still limited understanding of the pathogens associated with swine RD. In this study, we conducted a nationwide genomic surveillance on identifying viruses, bacteria, and antimicrobial resistance genes (ARGs) from the lungs of pigs with RD in China. By performing metatranscriptomic sequencing combined with metagenomic sequencing, we identified 21 viral species belonging to 12 viral families.

Bioregulatory event extraction using large language models: a case study of rice literature.

The extraction of biological regulation events has been a key focus in the field of biomedical nature language processing (BioNLP). However, existing methods often encounter challenges such as cascading errors in text mining pipelines and limitations in topic coverage from the selected corpus. Fortunately, the emergence of large language models (LLMs) presents a potential solution due to their robust semantic understanding and extensive knowledge base.

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively.

Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer.

It is vital to investigate the complex mechanisms underlying tumors to better understand cancer and develop effective treatments. Metabolic abnormalities and clinical phenotypes can serve as essential biomarkers for diagnosing this challenging disease. Additionally, genetic alterations provide profound insights into the fundamental aspects of cancer.

Fecal PCR survey and genome analysis of in China.

Proliferative enteropathy caused by is an important economic associated disease to pig industry, but the knowledge about the prevalence of in pig farms in China is limited. In addition, there is no complete genome sequence available for isolates from China. In this study, we developed a TaqMan qPCR for the screening of by targeting the bacterial 16S rDNA gene.

High-quality gene/disease embedding in a multi-relational heterogeneous graph after a joint matrix/tensor decomposition.

Motivation: Node embedding of biological entity network has been widely investigated for the downstream application scenarios. To embed full semantics of gene and disease, a multi-relational heterogeneous graph is considered in a scenario where uni-relation between gene/disease and other heterogeneous entities are abundant while multi-relation between gene and disease is relatively sparse. After introducing this novel graph format, it is illuminative to design a specific data integration algorithm to fully capture the graph information and bring embeddings with high quality.

OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition.

Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks.

A Novel Metric to Quantify the Effect of Pathway Enrichment Evaluation With Respect to Biomedical Text-Mined Terms: Development and Feasibility Study.

Background: Natural language processing has long been applied in various applications for biomedical knowledge inference and discovery. Enrichment analysis based on named entity recognition is a classic application for inferring enriched associations in terms of specific biomedical entities such as gene, chemical, and mutation.

Objective: The aim of this study was to investigate the effect of pathway enrichment evaluation with respect to biomedical text-mining results and to develop a novel metric to quantify the effect.