Genesis v1.7.6.30.09.10 Linux

peeasian  花野真衣  沖壓

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商品名稱： Genesis v1.7.6.30.09.10 Linux


商品分類： Linux系統專用軟體


商品類型： 對多個試驗的基因數據進行比較的軟體


語系版本： 英文正式版


運行平台： LINUX (以官方網站為準)


更新日期： 2010-11-11




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.