New database maps 5,600+ hidden disease-causing microbes using AI text mining
Researchers built an automated system that scanned 25,000 scientific papers to identify previously overlooked connections between opportunistic pathogens and human diseases. The resulting database could accelerate drug development and help hospitals predict infection risks in vulnerable patients.
Originaltitel: SigMine and OPathDb: a literature-mining pipeline and database of potential opportunistic pathogens.
SigMine-verktyget och OPathDb-databasen möjliggör screening av potentiella opportunistiska patogener genom automatiserad litteraturanalys — en kritisk möjlighet för drug discovery och diagnostikutveckling. Systemet parsed 25 000 artiklar från PubMed Central och identifierar statistiskt signifikanta associationer mellan patogener, sjukdomar, gener, metaboliter och vävnader. OPathDb innehåller 5 626 potentiella opportunistiska patogener länkade till 1 440 sjukdomar och 7 121 gener. Databasen visualiserar samband genom viktade nätverk — exempel inbegriper Akkermansia mucinifila med tjocktarmscancer och Segatella copri med glukosintolerans. Netaji Subhas University of Technology i Delhi och Uppsala University utvecklade plattformen med stöd från indiska bioteknikdepartementen. För FoU-ledare inom infektionsbiologi och personaliserad medicin erbjuder OPathDb en strukturerad resurs för att identifiera okända patogen-fenotyp-relationer och påskynda målvalidering i early-stage projektplanering.
Conversion of unstructured biomedical literature into structured knowledge for identifying cross-domain associations between biological entities remains a challenging task. SigMine is an automated pipeline constructed to mine biomedical literature to identify significantly associated biological entities. SigMine performs biomedical entity recognition from PMC articles using the EuropePMC Annotation API. Advanced entity recognition was performed using Python scripting, NCBI E-Utilities, and an n-gram algorithm followed by extensive data cleaning and mapping against standard databases. Statistical evaluation identified significantly co-occurring entities. The entire workflow was automated through a modular framework developed in Python v3.13 with a Tkinter-based Graphical User Interface. SigMine enhances usability while retaining the flexibility to use new dictionaries for annotation. SigMine was used to construct a literature-derived potential human Opportunistic Pathogens Database (OPathDb), housing 5,626 potential opportunistic pathogens significantly co-occurring with 1440 diseases and 7121 genes mined from 25,000 PMC articles. Additional annotation of 598 significantly co-occurring metabolites and 30 affected tissues is available for 3204 and 227 pathogens, respectively. OpathDb has a user-friendly query interface searchable by organism, disease, tissue, gene, protein and metabolite available at https://www.opathdb.cbsblab-nsut.in . Organism-entity associations can be visualized as weighted networks, with color-coded nodes and significance-scaled edges. Significant associations of opportunistic pathogens like Akkermansia mucinifila with colorectal cancer and Segatella copri with glucose intolerance can be identified through OpathDb. Through this database, the SigMine framework demonstrates conversion of unstructured text in vast and heterogenous corpora into standardized and well-organized information. Statistically inferred associations in OPathDb are potential candidates for clinical and experimental validation.