<?xml version="1.0"?>
<feed xmlns="http://www.w3.org/2005/Atom" xml:lang="uk">
		<id>http://istoriya.soippo.edu.ua/index.php?action=history&amp;feed=atom&amp;title=Reasons_Why_Most_People_Are_Talking_About_Venetoclax</id>
		<title>Reasons Why Most People Are Talking About Venetoclax - Історія редагувань</title>
		<link rel="self" type="application/atom+xml" href="http://istoriya.soippo.edu.ua/index.php?action=history&amp;feed=atom&amp;title=Reasons_Why_Most_People_Are_Talking_About_Venetoclax"/>
		<link rel="alternate" type="text/html" href="http://istoriya.soippo.edu.ua/index.php?title=Reasons_Why_Most_People_Are_Talking_About_Venetoclax&amp;action=history"/>
		<updated>2026-05-09T20:46:07Z</updated>
		<subtitle>Історія редагувань цієї сторінки в вікі</subtitle>
		<generator>MediaWiki 1.24.1</generator>

	<entry>
		<id>http://istoriya.soippo.edu.ua/index.php?title=Reasons_Why_Most_People_Are_Talking_About_Venetoclax&amp;diff=139686&amp;oldid=prev</id>
		<title>Salebabies1: Створена сторінка: It can help to find significantly affected pathways within a gene expression data. The IPAD contains about 22,498 genes, [http://www.selleckchem.com/products/ab...</title>
		<link rel="alternate" type="text/html" href="http://istoriya.soippo.edu.ua/index.php?title=Reasons_Why_Most_People_Are_Talking_About_Venetoclax&amp;diff=139686&amp;oldid=prev"/>
				<updated>2017-02-12T09:37:43Z</updated>
		
		<summary type="html">&lt;p&gt;Створена сторінка: It can help to find significantly affected pathways within a gene expression data. The IPAD contains about 22,498 genes, [http://www.selleckchem.com/products/ab...&lt;/p&gt;
&lt;p&gt;&lt;b&gt;Нова сторінка&lt;/b&gt;&lt;/p&gt;&lt;div&gt;It can help to find significantly affected pathways within a gene expression data. The IPAD contains about 22,498 genes, [http://www.selleckchem.com/products/abt-199.html selleck chemicals llc] 25,469 proteins, 1,956 pathways, 6,704 diseases, 5,615 drugs, and 52 organs integrated from databases including the BioCarta,17 KEGG,21 NCI-Nature curated,22 Reactome,23 CTD,24 PharmGKB,25 DrugBank,26 and HOMER.27 It can provide reliable pathway�Cgene relationship for the gene set database in GSEA. The 16 pathway-based biomarkers we identified are signaling, complement pathway, binding receptors, and metabolism (Table 1), which are consistent with previous findings.28 We further evaluated the prediction performance of our pathway-based biomarkers by comparing our results with prediction performances in previously published findings. For example, Aaroe et al identified a set of 738 differentially expressed probes that achieved an estimated prediction accuracy of 79.5% with a sensitivity of 80.6% and a specificity of 78.3%28 and Sharma et al identified a panel of 37 genes that permitted early detection with a classification accuracy of 82%.29 These prediction results were based on the training set, not the independent testing set. For the testing set, [http://en.wikipedia.org/wiki/Histone_demethylase Histone demethylase] our pathway-based biomarkers show a similar prediction performance to theirs. However, for the training set, prediction performance of our pathway-based biomarkers is higher than theirs (Table 2). In addition, we also did the feature selection based on all proteins. With a P value cutoff [http://www.selleckchem.com/products/MDV3100.html Enzalutamide purchase] = 90.00%, sensitivity = 92.50%, specificity = 87.50%) and for the testing set (AUC = 0.9188, precision = 81.25%, accuracy = 87.50%, sensitivity = 97.50%, specificity = 77.50%). The prediction performances based on proteins are lower than the highest performance of our pathway-based approach. When we chose the top 17 proteins in Study A as biomarkers, we obtained lower prediction performance (AUC = 0.8138 for the testing set) than the mean performance based on the pathway-based biomarkers (AUC = 0.8350 for the testing set). An interesting observation in our study is that some of genes in the pathway-based biomarker are not differentially expressed between cancer and normal, for example, in the GPCR downstream signaling pathway, ADRBK1 (P value = 0.68), AGT (P value = 0.94) and OR7D4 (P value = 0.69) with high a P value. After we removed all proteins with P value �� 0.001 in the pathway, the prediction performances dropped dramatically (Table 4). It suggests that the genes with a high P value can still be valuable in a pathway, compared with conventional methods, which usually limit genes to those with change below a P value threshold such as 0.001.&lt;/div&gt;</summary>
		<author><name>Salebabies1</name></author>	</entry>

	</feed>