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As per available reports about 43 relevant Journals, 54 Conferences, 10 Workshops, are presently dedicated exclusively to In silico elemental analysis and about 66 articles are being published on In silico elemental analysis.
In silico research in medicine is thought to have the potential to speed the rate of discovery while reducing the need for expensive lab work and clinical trials achieved by producing and screening drug candidates more effectively by the use of expensive high-throughput screening (HTS) robotic labs to physically test thousands of diverse compounds a day often with an expected hit rate on the order of 1% or less with still fewer expected to be real leads following further testing for Drug Discovery. Online open-source sequence data represents an excellent resource for identifying differential gene expression. In silico elemental resources are popular starting points in many disease gene discovery research programs.
In silico research in medicine is thought to have the potential to speed the rate of discovery while reducing the need for expensive lab work and clinical trials achieved by producing and screening drug candidates more effectively by the use of expensive high-throughput screening (HTS) robotic labs to physically test thousands of diverse compounds a day often with an expected hit rate on the order of 1% or less with still fewer expected to be real leads following further testing for Drug Discovery. Online open-source sequence data represents an excellent resource for identifying differential gene expression. In silico elemental resources are popular starting points in many disease gene discovery research programs. These datasets are collected and annotated in highly organized online databases. The modern biomedical investigator therefore has the ability to genomically profile diseases or distinctions of interest thereby identifying differentially expressed genes. Although many of the tools available to mine these collections, and particularly their interfaces, are quite basic in design, nevertheless represent an excellent resource for gene discovery. Furthermore, although these tools can act as good starting points in disease gene discovery there is a need for experimental validation of in silico-derived differential expression results. EST and SAGE libraries are not without their limitations. One limitation associated with the use of EST databases is that only highly expressed genes have been sampled adequately to provide sufficient corresponding EST counts for reliable molecular profiling. There is therefore a bias towards highly-expressed genes in libraries. Investigators must therefore be cognisant that expression profiles garnered from EST libraries may not contain these low abundance transcripts. As single-read sequences, ESTs are prone to sequencing error, although sequencing errors do not preclude identification of the original gene. Furthermore, the 5' ends of genes are underrepresented in EST databases. Libraries from which ESTs are derived can be contaminated with genomic material and using ESTs will not detect genes from tissues or cells, which are difficult to obtain mRNA from. ESTs omit introns which may contain important gene regulatory sequences. SAGE libraries are subject to variable tag specificity, and the restriction enzymes used in tag generation yield fragments of various lengths. The use of in silico gene mining strategies provides an excellent framework for the initial identification of key genes and gene clusters whose expression is altered in disease tissue. The data generated in these investigations provide a starting point for investigations aimed at delineating the molecular basis of disease.
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This page was last updated on November 5, 2024
