The single sample classifier (SSC) calculates the most likely iCMS class based on the class of the nearest centroid by non-parametric correlation distance. A confident call is made when the correlation is at least 0.1 and distance from the nearest centroid of the opposite iCMS class is greater...
Fig. 2: Three dose groups and their functions. AThe doses of three groups (low, medium, and high). Each point denotes the absorbed dose of a sample.BThe receiver operating characteristic (ROC) of dose grouping based on the KNN classifier.CThe number of DIGs in three dose groups.DThe V...
For building a pair-based classifier with a one-vs-rest scheme, we start by selecting top differentially expressed genes using thefilter_genes_TSPfunction. This reduces the number of gene combinations (rules) in the next steps. This function can perform the filtering in different ways and return...
However, the aim was to derive a classifier that can stratify patients into three groups: (1) those that could be spared RT, (2) those that benefit from and should be given RT and (3) those that are intrinsically radioresistant, and where other treatments strategies should be considered ...
nearest neighbor classifier is employed for classification.The experiment results on the JAFFE and ORL human face database indicate that weighted modular 2DPCA is superior to both conventional 2DPCA and modular 2DPCA in terms of accuracy and robustness with the same dimension of discriminate features...
Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM. Sci Rep. 2020;10(1):11019. Article CAS PubMed PubMed Central Google Scholar Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al....
Finallyby rotating the 3D face image, virtual samples with different views are generated.Experimental results on ORL dataset using nearest neighbor as classifier reveal animprovement about 5% in recognition rate for one sample per person by enlarging trainingset using generated virtual samples. Compared...
Conclusions Each single sample classifier method had strengths and limitations. The RF based approach emerged as a promising new method, being robust even when using very few genes, while also allowing for evaluation of class stability and putative incorrect reference labels during training. The main...
The method includes classifying each phenotype of the test data with a classifier, and assigning a phenotype to a patient.FILIPPO UTROALDO GUZMAN SAENZCHAYA LEVOVITZLAXMI PARIDA
PURITY INDEPENDENT SUBTYPING OF TUMORS (PURIST), A PLATFORM AND SAMPLE TYPE INDEPENDENT SINGLE SAMPLE CLASSIFIER FOR TREATMENT DECISION MAKING IN PANCREATIC CANCERProvided are methods for identifying pancreatic cancer subtypes in a subject and treating the same. In some embodiments, the method comprise ...