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Exploratory Data Analysis with Unsupervised Machine Learning
Published in Altuna Akalin, Computational Genomics with R, 2020
Principal component analysis (PCA) is maybe the most popular technique to examine high-dimensional data. There are multiple interpretations of how PCA reduces dimensionality. We will first focus on geometrical interpretation, where this operation can be interpreted as rotating the original dimensions of the data. For this, we go back to our example gene expression data set. In this example, we will represent our patients with expression profiles of just two genes, CD33 (ENSG00000105383) and PYGL (ENSG00000100504). This way we can visualize them in a scatter plot (see Figure 4.9).
Metabolic Disorders III
Published in John F. Pohl, Christopher Jolley, Daniel Gelfond, Pediatric Gastroenterology, 2014
Laurie A. Tsilianidis, David A. Weinstein, Roberto Zori
GSD VI (Hers disease) is caused by mutations in the PYGL gene located at 14q21–22. Although glycogen phosphorylase is the rate-limiting step in glycogenolysis, this is a mild disorder that presents with ketotic hypoglycemia or hepatomegaly.
Diabetes area patent participation analysis – part II: years 2011-2016
Published in Expert Opinion on Therapeutic Patents, 2018
Markus Boehm, Matthew Crawford, Jamie E. Moscovitz, Philip A. Carpino
A number of targets from 2008–2010 exhibited no patent activity in the 2011–2016 time period (data not shown). These included several well-known targets that were once active areas of drug discovery research among pharmaceutical companies, for example, BRS3, CPT2, FBP1, NPY5R, and PYGL.