Jindent v4.1.0 for Unix

office 2010  花野真衣  星際大戰

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商品名稱： Jindent v4.1.0 for Unix

商品分類： 程式開發、數據.資料庫系統

商品類型： Java源代碼格式軟體

語系版本： 英文正式版

運行平台： Windows XP/Vista/7

更新日期： 2010-11-11


破解說明:


check crack\install.txt
內容說明:


Jindent 是一款功能強大的Java源代碼格式工具，Jindent按照你獨特的偏好使JAVA
變得漂亮或傳遞代碼到第三方代碼環境中。4.0版支援超過300種格式化選項，以及
Java 5.0中的所有語言特性，包括泛型等等。此外Jindent 4.0還提供了改進的GUI
設計和針對主要作業系統的本地安裝程式。
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.