ArticleBMC research notes2016
Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.
Article in BMC research notes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 31 citations in OpenAlex.
- Article
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- Identifying and Validating Networks of Oncology Biomarkers Mined From the Scientific Literature.Cancer informatics · 2022Article
- Epione application: An integrated web‑toolkit of clinical genomics and personalized medicine in systemic lupus erythematosus.International journal of molecular medicine · 2022Article
- Text Mining for Building Biomedical Networks Using Cancer as a Case Study.Biomolecules · 2021Review
- Mining Proteome Research Reports: A Bird's Eye View.Proteomes · 2021Article
- Demetra Application: An integrated genotype analysis web server for clinical genomics in endometriosis.International journal of molecular medicine · 2021Article
- Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization.BMC medical informatics and decision making · 2020Article
- Edgetic perturbation signatures represent known and novel cancer biomarkers.Scientific reports · 2020Article
- Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach.Frontiers in genetics · 2019Article
- Tracking knowledge evolution, hotspots and future directions of emerging technologies in cancers research: a bibliometrics review.Journal of Cancer · 2019Review
- Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson.PloS one · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer.
resultsWe utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries.
conclusionsInteresting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.