Article

Automated literature mining and hypothesis generation through a network of Medical Subject Headings

2018-08-29

Abstract excerpt

<h4>ABSTRACT</h4> The scientific literature is vast, growing, and increasingly specialized, making it difficult to connect disparate observations across subfields. To address this problem, we sought to develop automated hypothesis generation by networking at scale the MeSH terms curated by the National Library of Medicine. The result is a Mesh Term Objective Reasoning (MeTeOR) approach that tallies associations a...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 20850d61-980d-5502-8bd6-97376a3face5
DOI: 10.1101/403667

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Automated literature mining and hypothesis generation through a network of Medical Subject HeadingsDOI 10.1101/403667Read

SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis EvaluationSemantic proximity Read

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer ModelsSemantic proximity Read

Machine learning approach for the binary classification of biomedical literatureSemantic proximity Read

Variantscape: Large Language Model-Driven Mining of Biomedical Literature for Clinical Interpretation of Cancer VariantsSemantic proximity Read

Ontology-driven association rule mining for biomedical entity relationships: integrating hierarchical knowledge to improve gene-disease discovery.Semantic proximity Read

dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene InteractionsSemantic proximity Read

LLM-Based Classification of Case Report Abstracts: A Pilot Study on Interactions between Radiotherapy and Systemic TherapiesSemantic proximity Read

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learningSemantic proximity Read

Accelerating COVID-19 research with graph mining and transformer-based learningSemantic proximity Read

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational researchSemantic proximity Read

Organizing knowledge to enable faster data interpretation in COVID-19 researchSemantic proximity Read

megaMine: a scalable, rule-based framework for mining gene-cancer-drug evidence from biomedical literatureSemantic proximity Read