ArticleMolecular systems biology2023
Automated assembly of molecular mechanisms at scale from text mining and curated databases.
Article in Molecular systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of air pollution on COVID-19 severity: a systematic review of underlying biological mechanisms.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Applying prior knowledge of regulatory signaling to investigate macrophage cAMP dynamics during Mycobacterium tuberculosis infection.Scientific reports · 2026Article
- Towards the construction of a virtual yeast.Nature · 2026Review
- Functional Mapping of Neurodevelopmental Disease Pathways to Key Neurodevelopmental Processes Represented in the Developmental Neurotoxicity In Vitro Testing Battery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- eGoT: enhanced graph-of-thoughts for multi-hop knowledge retrieval and hypothesis generation in biomedicine.Bioinformatics (Oxford, England) · 2026Article
- Single-Cell Proteomic Technologies: Tools in the Quest for Principles.Annual review of biophysics · 2026Review
- Artificial intelligence revolutionizing CNS drug discovery and development.Drug discovery today · 2026Review
- GPU-accelerated modeling of biological regulatory networks.Scientific reports · 2026Article
- The SjD Map: an interactive pathway tour into Sjögren's disease signalling mechanisms.NPJ systems biology and applications · 2026Article
- Semantic annotation of Glycomics and Glycoproteomics methods.Glycobiology · 2026Article
- An Integrated Text Mining Approach for Discovering Pharmacological Effects, Drug Combinations, and Repurposing Opportunities of ACE Inhibitors.International journal of molecular sciences · 2026Article
- textToKnowledgeGraph: generation of molecular interaction knowledge graphs using large language models for exploration in Cytoscape.Bioinformatics (Oxford, England) · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- SIGNOR 4.0: the 2025 update with focus on phosphorylation data.Nucleic acids research · 2026Article
- OmniPath: integrated knowledgebase for multi-omics analysis.Nucleic acids research · 2026Article
- MetaphorPrompt2-A Structure and Function-Focused Approach for Extracting Causal Events from Biological Text.Computational and structural biotechnology journal · 2026Article
- Literature-driven extraction and computational prediction of causal statements linking genetic variants to biological processes, pathways and phenotypes.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Assembly and reasoning over semantic mappings at scale for biomedical data integration.Bioinformatics (Oxford, England) · 2025Article
- Adipocyte/Tumor cell crosstalk via IGF-1/TXNIP axis promotes malignancy and endocrine resistance in breast cancer.Cell communication and signaling : CCS · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine-reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non-redundant and broadly usable mechanistic knowledge. Using INDRA to create high-quality corpora of causal knowledge we show it is possible to extend protein-protein interaction databases and explain co-dependencies in the Cancer Dependency Map.
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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 OpenQuestion graph.