ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2008
Enabling integrative genomic analysis of high-impact human diseases through text mining.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Disease signatures are robust across tissues and experiments.Molecular systems biology · 2009Pooled it
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Systematic tissue annotations of genomics samples by modeling unstructured metadata.Nature communications · 2022Article
- Extending TCGA queries to automatically identify analogous genomic data from dbGaP.F1000Research · 2017Article
- A systematic assessment of linking gene expression with genetic variants for prioritizing candidate targets.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- Clinic-genomic association mining for colorectal cancer using publicly available datasets.BioMed research international · 2014Article
- Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE.BMC systems biology · 2012Article
- Integrative approach to pain genetics identifies pain sensitivity loci across diseases.PLoS computational biology · 2012Article
- Comparison of automated and human assignment of MeSH terms on publicly-available molecular datasets.Journal of biomedical informatics · 2011Article
- Recent advances in biomarker discovery in solid organ transplant by proteomics.Expert review of proteomics · 2011Review
- Discovery and preclinical validation of drug indications using compendia of public gene expression data.Science translational medicine · 2011Article
- Computational prediction and experimental validation associating FABP-1 and pancreatic adenocarcinoma with diabetes.BMC gastroenterology · 2011Article
- DSGeo: software tools for cross-platform analysis of gene expression data in GEO.Journal of biomedical informatics · 2010Article
- Differentially expressed RNA from public microarray data identifies serum protein biomarkers for cross-organ transplant rejection and other conditions.PLoS computational biology · 2010Article
- Recall and bias of retrieving gene expression microarray datasets through PubMed identifiers.Journal of biomedical discovery and collaboration · 2010Article
- Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets.PLoS computational biology · 2010Article
- Towards large-scale sample annotation in gene expression repositories.BMC bioinformatics · 2009Article
- Evaluation of a large-scale biomedical data annotation initiative.BMC bioinformatics · 2009Article
- Translational bioinformatics applications in genome medicine.Genome medicine · 2009Article
- TRANSLATING BIOLOGY: TEXT MINING TOOLS THAT WORK.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2008Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Our limited ability to perform large-scale translational discovery and analysis of disease characterizations from public genomic data repositories remains a major bottleneck in efforts to translate genomics experiments to medicine. Through comprehensive, integrative genomic analysis of all available human disease characterizations we gain crucial insight into the molecular phenomena underlying pathogenesis as well as intra- and inter-disease differentiation. Such knowledge is crucial in the development of improved clinical diagnostics and the identification of molecular targets for novel therapeutics. In this study we build on our previous work to realize the next important step in large-scale translational discovery and analysis, which is to automatically identify those genomic experiments in which a disease state is compared to a normal control state. We present an automated text mining method that employs Natural Language Processing (NLP) techniques to automatically identify disease-related experiments in the NCBI Gene Expression Omnibus (GEO) that include measurements for both disease and normal control states. In this manner, we find that 62% of disease-related experiments contain sample subsets that can be automatically identified as normal controls. Furthermore, we calculate that the identified experiments characterize diseases that contribute to 30% of all human disease-related mortality in the United States. This work demonstrates that we now have the necessary tools and methods to initiate large-scale translational bioinformatics inquiry across the broad spectrum of high-impact human disease.
Indexed as
Identifiers
18229717PMC2735266What 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.