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Bioinformatics involves the manipulation, searching, and data mining of biological data, and this includes DNA sequence data.
This may facilitate many aspects of in-depth mining of biological information relevant to the fenretinide-induced apoptosis.
Our robust transcriptome profiling approach facilitated the in-depth mining of biological information relevant to oxidative stress-mediated apoptosis, including the prediction of upstream transcription factors involved in gene regulation.
Nevertheless, mining of biological and medical literature is a very challenging task [ 5- 7].
Winck et al. [ 23] obtained encouraging results by applying a context-based preprocessing to data mining of biological text.
Nowadays mining of biological, biomedical, and healthcare data is a fast evolving research area at the intersection between biology and data mining, thanks to unprecedented data deluge in the biomedical research.
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It involved text mining of the biological literature, as all identified TFs were investigated (screening of MEDLINE entries).
This work is aimed at data mining of the biological test results generated by the NIH MLP initiative by analyzing the target specificity properties of small molecules.
Many tools are available for mining of heterogeneous biological data, although the focus of such tools and the challenges being addressed by them have largely been in the domains of molecular interactions and biological pathways [ 53].
The Commission explains that the Human Brain Project will "collect the masses of clinical data available, mining for biological patterns, leading to new ways of diagnosing and classifying brain diseases".
Liao and Chen [ 18] presented the Depth-First Spelling algorithm for mining sequential patterns of biological sequences with Gap constraints (termed DFSG).
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