Sentence examples for molecular classification method from inspiring English sources
We describe a novel molecular classification method for medulloblastoma that relies on the nanoString nCounter System.
Another sequence-based molecular classification method for viruses is based on pairwise identities of virus sequences within a virus family.
These four clinical subsets correspond closely (≥80% concordance) to groups defined by various molecular classification methods that rely on gene expression analysis or immunohistochemistry.
This admixture can mask the signal from the cancer cells and thus complicate the inter- and intra-tumor comparisons, which are the basis of molecular classification methods.
The schema attests for the robustness of the original concept that all the different molecular classification methods tend to assign individual patients, with reasonably high concordance, to the same molecular subtype.
Together with other molecular classification methods [ 5, 6], these data indicate that the identification of differential gene expression has great potential for improved prediction of disease outcome and subsequent treatment decisions.
A parallel study was designed to address experimental issues of combining microarrays and HR MAS MRS. In the first strategy, using the microarray data and previously reported molecular classification methods, the majority of samples were classified as luminal A. Three subgroups of luminal A tumors were identified based on hierarchical clustering of the HR MAS MR spectra.
The step-by-step filtration method, along with a molecular weight classification method, was selected to build the chemical oxygen demand (COD) and biochemical oxygen demand (BOD) fingerprints of petrochemical wastewater and treated wastewater.
The workflow here provides an objective means for the development of molecular-based classification method that was not possible with previous methods such as single gene, arbitrary multiple gene approaches, VNTR as well as MLSA.
We then compared these lists to a matrix of 90 user-defined gene sets (Table 3) belonging to various molecular classifications (see methods for details).
However, A typical molecular sub-classification method for lung carcinomas would have a low predictive accuracy of 68%-71 68%-71se datasets of gene-expression profiles typically have tens of thousands of genes for just few hundreds of patients.
