ArticleNucleic acids research2012
Analyzing large biological datasets with association networks.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
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Who cites it
14 citing papers in PubMed.
- Microbial Diversity and Composition Is Associated with Patient-Reported Toxicity during Chemoradiation Therapy for Cervical Cancer.International journal of radiation oncology, biology, physics · 2020Trial
- Comparative genomics of Bacillus velezensis and Bacillus subtilis reveals distinction and evolution of lipopeptide antimicrobial gene clusters.BMC genomics · 2025Article
- Immune environment and antigen specificity of the T cell receptor repertoire of malignant ascites in ovarian cancer.PloS one · 2023Article
- Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications.Frontiers in bioinformatics · 2021Article
- Tools for Analysis of the Microbiome.Digestive diseases and sciences · 2020Review
- Aerobic prokaryotes do not have higher GC contents than anaerobic prokaryotes, but obligate aerobic prokaryotes have.BMC evolutionary biology · 2019Article
- Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.Frontiers in microbiology · 2018Article
- A family of interaction-adjusted indices of community similarity.The ISME journal · 2017Article
- Practical Approaches for Mining Frequent Patterns in Molecular Datasets.Bioinformatics and biology insights · 2016Review
- Insights from 20 years of bacterial genome sequencing.Functional & integrative genomics · 2015Review
- A primer to frequent itemset mining for bioinformatics.Briefings in bioinformatics · 2015Review
- Network analysis in the identification of special mechanisms between small cell lung cancer and non-small cell lung cancer.Thoracic cancer · 2014Article
- RapidMic: Rapid Computation of the Maximal Information Coefficient.Evolutionary bioinformatics online · 2014Article
- Mining rare associations between biological ontologies.PloS one · 2014Article
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
No grant is acknowledged in the PubMed record.
Abstract
Due to advances in high-throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches that process collected data into new knowledge in a timely manner. In this study, we propose a computational framework for discovering modular structure, relationships and regularities in complex data. The framework utilizes a semantic-preserving vocabulary to convert records of biological annotations of an object, such as an organism, gene, chemical or sequence, into networks (Anets) of the associated annotations. An association between a pair of annotations in an Anet is determined by the similarity of their co-occurrence pattern with all other annotations in the data. This feature captures associations between annotations that do not necessarily co-occur with each other and facilitates discovery of the most significant relationships in the collected data through clustering and visualization of the Anet. To demonstrate this approach, we applied the framework to the analysis of metadata from the Genomes OnLine Database and produced a biological map of sequenced prokaryotic organisms with three major clusters of metadata that represent pathogens, environmental isolates and plant symbionts.
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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.