PubMed · 14214040
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
R L SISSON. 1964-07-31. COMPUTER OUTPUT AND DISPLAY DEVICES.. https://pubmed.ncbi.nlm.nih.gov/14214040/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
BACKGROUND: The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public. RESULTS: We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. CONCLUSIONS: Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users.
Recent advances in human and molecular genetics provide an unparalleled opportunity to understand how genes and genetic changes interact with environmental stimuli to either preserve health or cause disease. The fields of environmental genetics and environmental genomics has enormous potential to affect our ability to accurately assess the risk of developing disease, identify and understand basic pathogenic mechanisms that are critical to disease progression, and to more precisely phenotype disease subtypes. However, the application of genetics and genomics to problems in environmental health is only the beginning yet, by itself, represents a potentially effective strategy to substantially impact morbidity and mortality. Collaborative approaches that team together environmental scientists with molecular biologists, geneticists, physiologists and physician scientists are critical to the investigation of environmental aspects of human health. Moreover, exploiting eukaryotic model systems (yeast, Caenorhabditis elegans, zebrafish, Drosophila and rodents) will accelerate our understanding of environmental exposures on human health.