PMC:4996407 / 19766-21692 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4996407","sourcedb":"PMC","sourceid":"4996407","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4996407","text":"2.3.4. One-Against-All (Orthogonality)\nOne of the difficulties of using whole blood is that its transcriptome contains information reflecting not only many diseases but also all kinds of other confounding factors. However, this breadth of information is also the key to the solution of the problem. Since so many factors are mirrored in the blood transcriptome, it should be possible to find a signature for each, and reduce the problem to a set of “independent” or orthogonal equations for which the solution becomes nearly trivial.\nThis solution is based on our hypothesis that, whereas many genes are affected by more than one disease or condition, there may exist combinations of genes that are affected only by a single condition. By setting up an analysis to look for only those combinations of genes that respond to a single condition in the explicit presence of confounding factors such as other diseases, we will be able to identify those genes that match the independent equation case. One beneficial side effect of this solution is that sample acquisition becomes much simpler; it is not necessary to find samples from patients with two or more diseases or conditions of interest. The practical consequence is not trivial: for diseases with very low prevalence rates, patients with multiple disease combinations would be vanishingly rare and impossible to acquire.\nWe assign the “other” samples to the “not-this-disease” group and take advantage of the relatively larger numbers to attenuate any “out of the ordinary” characteristics of an individual sample. As the number increases, the potential skew from any one individual is diluted. Additionally, the gene panel is trained to reject the signature of all other diseases included in the “other” samples. This is the “one-against-all” approach for analysing gene expression profiles that makes each prediction panel more specific to the target disease condition.","divisions":[{"label":"Title","span":{"begin":0,"end":38}}],"tracks":[]}