Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics.
ABSTRACT Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.
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ABSTRACT: Meningioma is a frequently occurring tumor of the meninges surrounding the central nervous system. Loss of the short arm of chromosome 1 (1p) is the second most frequent chromosomal abnormality observed in these tumors. Previously, we identified a 3.7 megabase (Mb) region of consistent deletion on 1p33-p34 in a panel of 157 tumors. Loss of this region was associated with advanced disease and predictive for tumor relapse. In this report, a high-resolution integrated map of the region was constructed (CompView) to identify all markers in the smallest region of overlapping deletion (SRO). A regional somatic cell hybrid panel was used to more precisely localize those markers identified in CompView as within or overlapping the region. Additional deletion mapping using microsatellites localized to the region narrowed the SRO to approximately 2.8 Mb. The 88 markers remaining in the SRO were used to screen genomic databases to identify large-insert clones. Clones were assembled into a physical map of the region by PCR-based, sequence-tagged site (STS) content mapping. A sequence from clones was used to validate STS content by electronic PCR and to identify transcripts. A minimal tiling path of 43 clones was constructed across the SRO. Sequence data from the most current sequence assembly were used for further validation. A total of 59 genes were ordered within the SRO. In all, 17 of these were selected as likely candidates based on annotation using Gene Ontology Consortium terms, including the MUTYH, PRDX1, FOXD2, FOXE3, PTCH2, and RAD54L genes. This annotation of a putative tumor suppressor locus provides a resource for further analysis of meningioma candidate genes.Oncogene 02/2004; 23(4):1014-20. · 6.37 Impact Factor
New England Journal of Medicine 04/2003; 348(10):881-2. · 53.30 Impact Factor