ArticleArchives of microbiology2026
SigMine and OPathDb: a literature-mining pipeline and database of potential opportunistic pathogens.
Article in Archives of microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
Funding
Abstract
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.
Indexed as
Identifiers
42435073What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.