軟體王

Genesis v1.7.6.30.09.10 Linux 英文正式版(對多個試驗的基因數據進行比較的軟體)

破解說明:
check crack\install.txt
內容說明:
基因學方面的一款極具價值的工具包，主要特點如下：使用靈活，帶有多種分析工具，
視覺化的數據展示，附帶平台獨立的Java工具包，可以同時分析並展現一整套基因表
達試驗。通過讀取普通文件中的數據，可以生成圖形化的分析數據，從而能方便的對
多個試驗的基因數據進行比較。
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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