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Genesis Client Server v1.7.1 英文正式版(管理Genesis應用伺服器軟體)

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Genesis Client Server v1.7.1 英文正式版(管理Genesis應用伺服器軟體)




Genesis Client Server v1.7.1 英文正式版(管理Genesis應用伺服器軟體)



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Genesis Client Server v1.7.1 英文正式版(管理Genesis應用伺服器軟體)
^^^^^^^^^^^^^^^^^^^^^^^^ 2006年11月最新版!
For WIN 9x/WIN ME/WIN NT/WIN 2000/WIN XP/WIN 2003

原版光碟!!!絕對超值!!
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Genesis Client Server v1.7.1 英文正式版(管理Genesis應用伺服器軟體)

相關網址:
http://genome.tugraz.at/Software/Ge...rverClient.html

安裝序號:
Unpack and install the software
Use the following information to register

KpL@-e2P!-HAE&-Q#IB-D!Ti-snY2-8Tkt-@N46

DIGERATI

破解說明:

中文化說明:

內容說明:
Genesis Server Client是用於管理Genesis應用伺服器的一款軟體。它可用於統計,任
務處理及用戶管理.

英文說明:
High throughput gene expression analysis is becoming more
and more important in many areas of biomedical research.
cDNA microarray technology is one very promising approach
for high throughput analysis and gives the opportunity to
study gene expression patterns on a genomic scale.
Thousands or even tens of thousands of genes can be
spotted on a microscope slide and relative expression
levels of each gene can be determined by measuring the
fluorescence intensity of labeled mRNA hybridized to the
arrays. Hence, microarrays can be used to identify
differentially expressed genes in two samples on a large scale.

Beyond simple discrimination of differentially expressed
genes, functional annotation (guilt-by-association) or
diagnostic classification requires the clustering of genes
from multiple experiments into groups with similar
expression patterns. Several clustering techniques were
recently developed and applied to analyze microarray data.

We have developed a platform independent Java package of
tools to simultaneously visualize and analyze a whole set
of gene expression experiments. After reading the data
from flat files several graphical representations of
hybridizations can be generated, showing a matrix of
experiments and genes, where multiple experiments and
genes can be easily compared with each other. Fluorescence
ratios can be normalized in several ways to gain a best
possible representation of the data for further
statistical analysis.

We have implemented hierarchical and non hierarchical
algorithms to identify similar expressed genes and
expression patterns, including: 1) hierarchical
clustering, 2) k-means, 3) self organizing maps, 4)
principal component analysis, and 5) support vector
machines. More than 10 different kinds of similarity
distance measurements have been implemented, ranging from
simple Pearson correlation to more sophisticated
approaches like mutual information. Moreover, it is
possible to map gene expression data onto chromosomal
sequences. The flexibility, variety of analysis tools and
data visualizations, as well as the free availability to
the research community makes this software suite a
valuable tool in future functional genomic studies.

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Genesis Client Server v1.7.1 英文正式版(管理Genesis應用伺服器軟體)

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  (2010-02-08 10:49)
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