ReviewCancer informatics2014
Text mining in cancer gene and pathway prioritization.
Review in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed.
- Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.PloS one · 2024Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Deep learning for cancer type classification and driver gene identification.BMC bioinformatics · 2021Article
- DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases.Oxidative medicine and cellular longevity · 2020Article
- Cancer classification and pathway discovery using non-negative matrix factorization.Journal of biomedical informatics · 2019Article
- Literature-Based Enrichment Insights into Redox Control of Vascular Biology.Oxidative medicine and cellular longevity · 2019Review
- Using natural language processing and machine learning to identify breast cancer local recurrence.BMC bioinformatics · 2018Article
- A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.PLoS computational biology · 2018Article
- Review
- Recurrent neural networks for classifying relations in clinical notes.Journal of biomedical informatics · 2017Article
- Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach.Oncotarget · 2017Article
- Contralateral Breast Cancer Event Detection Using Nature Language Processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations.Briefings in bioinformatics · 2017Article
- Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records.Biomedical informatics insights · 2016Article
- Allelic Spectra of Risk SNPs Are Different for Environment/Lifestyle Dependent versus Independent Diseases.PLoS genetics · 2015Article
- Constructing a molecular interaction network for thyroid cancer via large-scale text mining of gene and pathway events.BMC systems biology · 2015Article
- Natural Language Processing for EHR-Based Computational Phenotyping.IEEE/ACM transactions on computational biology and bioinformaticsReview
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
Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.
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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.