Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Chapter 16: text mining for translational bioinformatics.

PLoS computational biology - 1 Apr 2013

Cohen K Bretonnel, Hunter Lawrence E

Abstract excerpt

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Artificial Intelligence
Share to Computational Biology

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Computer Simulation

Share to Data Mining

Share to Programming Languages

Share to Software

Share to Translational Research, Biomedical

Source

Open in PubMed Open full text

Identifiers

PMID: 23633944
DOI: 10.1371/journal.pcbi.1003044
PMCID: PMC3635962

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Chapter 16: text mining for translational bioinformatics.PMID 23633944Read ↗

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.Semantic proximity Read ↗

Mining gene-centric relationships from literature: the roles of gene mutation and gene expression in supporting drug discovery.Semantic proximity Read ↗

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

Generating Real-world Evidence from Unstructured Clinical Notes to Examine Clinical Utility of Genetic Tests: Use Case in BRCAnessSemantic proximity Read ↗

Reasoning Models for Text Mining in Oncology – a Comparison Between o1 Preview and GPT-4oSemantic proximity Read ↗

eGARD: Extracting associations between genomic anomalies and drug responses from textSemantic proximity Read ↗

Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.Semantic proximity Read ↗

Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.Semantic proximity Read ↗

Building Large-Scale Registries from Unstructured Clinical Notes using a Low-Resource Natural Language Processing PipelineSemantic proximity Read ↗

Comparative Study of Machine Learning Models for Textual Medical Notes ClassificationSemantic proximity Read ↗

PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites.Semantic proximity Read ↗

Intrinsic evaluation of text mining tools may not predict performance on realistic tasks.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.