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This work demonstrates that prediction of phosphorylation site can be more accurate for most kinase families if we incorporate more biological knowledge in the classification model.
However as more biological knowledge is accumulated and deposited into databases such as TRANSFAC[38] and JASPAR[39], we believe that TRANSMODIS will find more applications in the future.
We can minimize the modeling error with the accumulation of more biological knowledge.
As more biological knowledge and genomic data become publicly available and more easily accessible, we will continue to see methodological developments exploit this information to better dissect the genetic architecture of common, complex disease.
Therefore more datasets and more biological knowledge are needed to validate the results, and our future study will combine large-scale data from a variety of analyses at the SNP, gene, methylation alteration, and protein levels.
These ideas can naturally be extended by incorporating more biological knowledge into the model, for instance in the form of regulatory networks, partly assumed and partly learned from data.
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Hence, in order to achieve a more convincible conclusion, further analysis using larger sample size and more complete biological knowledge base and pathway information is required, and stratified analyses on different factors such as age, sex, disease severity, and platform usage are needed.
Given that the analysis requires high sensitivity, it would however be more advantageous to incorporate more biological expert knowledge in the selection process, such as the presence of the hairpin in an intron, sequence similarity to known miRNAs, etc.
We find a slow movement from global to local analysis, arguing that this trend emerged from a need to draw more concrete biological knowledge from networks.
Together, these results suggest that anthropocentric thinking is a common cognitive construal used when faced with a lack of more specific biological knowledge.
Several studies have subsequently been performed with the objective to unravel intermediary phenotype of these loci to obtain a more detailed biological knowledge initially seeking to break the diabetes-related phenotype for each locus into the major components of type 2 diabetes pathogenesis being insulin release, insulin sensitivity, and obesity.
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more biological explanation
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