Impact Factor In Datamining In Proteomics
Data
mining is the search for hidden trends within large sets of data. Data
mining approaches are needed at all levels of
genomics and proteomics analyses. These studies can provide a wealth of
information and rapidly generate large quantities of data from the analysis of biological specimens from healthy and diseased tissues. The high dimensionality of data generated from these studies will require the development of improved
bioinformatics and
computational biology tools for efficient and accurate data analyses. This issue of the Journal of
Biomedicine and
Biotechnology consists of seventeen papers that describe different applications of data
mining to both
genomics and
proteomics studies in yeast, and
plant and human
cells and tissues. Papers by Bensmail et al, Ghosh and Chinnaiyan, and Mao et al present different classification and clustering approaches for disease
biomarkers discovery.
Genomics and
proteomics studies have shown great promises and have been applied to studies aiming at generating expression profiles and elucidating expression networks in different organisms as shown in the papers by Samsa et al, Mungur et al, Liu et al, Baldwin et al, and Joy et al. Data
mining in
genomics and
proteomics studies reveals new regulatory pathways and mechanisms in different
health and disease conditions as presented by Wren and Garner, and provides comparative sequence analysis.
High Impact List of Articles
Relevant Topics in Clinical