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用 VisualBasic 编写的知识图谱数据库引擎

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在脚本中导入R#包,然后创建一个空的图形数据库: import graphQL kb = MsgFile::open()
RESEARCH Open Access
cytoHubba: identifying hub objects and sub-
networks from complex interactome
Chia-Hao Chin
1
, Shu-Hwa Chen
2
, Hsin-Hung Wu
6
, Chin-Wen Ho
5
, Ming-Tat Ko
2,6*
, Chung-Yen Lin
2,3,4*
From Asia Pacific Bioinformatics Network (APBioNet) Thirteenth International Conference on Bioinformatics
(InCoB2014)
Sydney, Australia. 31 July - 2 August 2014
Abstract
Background: Network is a useful way for presenting many types of biological data including protein-protein
interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network
features to infer their importance in the network, and it can help us identify central elements of biological
networks.
Results: We introduce a novel Cytoscape plugin cytoHubba for ranking nod es in a network by their network
features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component,
Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality
and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) ba sed on shortest paths.
Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of
predicting essential proteins from the yeast PPI network.
Conclusions: CytoHubba provide a user-friendly interface to explore important nodes in biological networks. It
computes all eleven methods in one stop shopping way . Besides, researchers are able to combine cytoHubba with
and other plugins into a novel analysi s scheme. The network and sub-networks caught by this topological analysis
strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental
biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around
6,700 times since 2010.
Background
Recent breakthroughs in high-throughput techniques
lead experimental data deluges in genomics, proteomics,
transcriptomics, metabolomics and interactomics. These
data can be represented as netwo rks, in which the nodes
as surrogates for proteins, metabolites, or transcripts, are
connected by edges to show the interactions, reactions,
or regulations among nodes. Network analysis can help
us understand the fu nction of an individual node and th e
collaboration between other nodes. For example, network
centralities rank nodes of a biological network acco rding
to a given importance concept, and Jeong et al.applied
this method on a protein-protein interaction network of
bakers yeast (Saccharomyces cerevisiae)[1].Theyfound
tha t the degree of a protein correlates with the essential-
ity of its gene; in other word, proteins with higher
degrees are more likely to be essential proteins.
Cytoscape [2] is an open platform with many plugins to
expand both the visualization options and the network
analysis power. Via Cytoscape, the graphical view of a net-
work is easy accessed, and multiple layers o f information
including large-scale, genome-wise experiments, and pro-
tein function annotations can be granted on the interac-
tome. Several Cytoscape plugins can score and rank the
nodes by network features. For example, NetworkAnalyzer
[3] and CentiScaPe [4] computes various topological net-
work parameters for undirected and/ or directed networks.
* Correspondence: mtko@iis.sinica.edu.tw; cylin@iis.sinica.edu.tw
Contributed equally
2
Institute of Information Science, Academia Sinica, No. 128 Academia Rd.,
Sec. 2, Taipei 115, Taiwan
Full list of author information is available at the end of the article
Chin et al. BMC Systems Biology 2014, 8(Suppl 4):S11
http://www.biomedcentral.com/1752-0509/8/S4/S11
© 2014 Chin et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Co mmons
Attribution License (http://creative commons.org/licenses/by/4.0), which permits unrestricted use, distribution, and re production in
any medium, provided the original work is properly cited. The Creative Commons Public Domain Ded ication waiv er (http://
creativecom mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
These plugins provide more centrality measures than
other commonly used, but some other important features
and recent developed methods are not included. Different
methods focus on different topological features, or similar
features with different scoring strategies. To make the net-
work analysis easier for biologists to utilize more network
features, we compose cytoHub ba plugin to execute our
newly developed algorithms and several popular algo-
rithms. The enhanced node retrieving function in cyto-
Hubba control panel helps researchers to search and
explore the network and to extract user interesting
subnetwork.
Results and discussion
The Usage of cytoHubba
CytoHubba provides a simple interface to analyze a net-
work with eleven scoring methods. First, scores from all
eleven methods are granted to each node in a preloaded
PPI network by e xecuting compute hubba result func-
tion in the cytoHubb a options in cytoscape menu bar
[plugins]. Next, top-ranked nodes of a particular scoring
method are retrie ved from the cytoHubba tab in Cytos-
cape control panel, listed in the result panel, and the sub-
graph of these selected nodes are shown in the main win-
dow with a color scheme from highly essential (re d) to
essential (green). The sub-graph of essential nodes is
extendable to includ e nodes that directly interact wit h
these top-ranked nodes by the option of check first stage
node in control panel | hubba. Network topological fea-
tures of nodes ar e retrievable in the data panel as options
of node attributes. Tutorials and demo video are available
in the website (http://hub.iis.sinica.edu.tw/cytohubba).
An example of cytoHubba result using the Cytoscape
example dataset galFiltered.cys is shown in Figure 1.
CytoHubba control panel is also a handy tool to
retrieve subnetwork from the whole big PPI set. A list
of nodes can be extracted by an ID list from the whole
hubba-computed network. This manipulation can be
extended to include direct linking partners (check on
the option check first st age node ), saved, and re-sub-
mitted to cytoHubba to evaluate the node essentiality on
the selected sub-network. For th ose nodes with no
Figure 1 T wo s cree n-sh ots showing centra lity analysi s wi th cytoHu bba.(A)AcytoHubba analysis result using the example dataset
galFiltered.cys in the Cytoscape. After the calculation (from the Cytoscape menu bar [plugins] ® cytoHubba ® compute hubba result), top 10
essential nodes ranked by MCC scores were selected (control panel | hubba | ® select nodes® make check on hubba nodes"® select a
method from choose a ranking method and determine how many nodes are selected from top group ® [submit])from the yeast network.
(B) Node and edge annotations are accessible through Cytoscape Data Panel.
Chin et al. BMC Systems Biology 2014, 8(Suppl 4):S11
http://www.biomedcentral.com/1752-0509/8/S4/S11
Page 2 of 7
direct link in between, cyt oHubba provides a shortest
path detect ion tool (display the shortest path on the
display option). All connectible but not direct connected
node pairs in a network, retrieved either by ID search or
by top-ranked in to pological feature score, ar e con-
nected by dotted-lines with number of the smallest edge
number (shortest path) to make thi s link. The stepping-
stone nodes and edges composing the shortest path will
be expanded by a mouse right-click action. Comparing
with the other cytoscape plugin Short estPath which
sketches the path between two nodes [5], cytoHubba
fetches the s hortes t path among a group of nodes. This
abstractive view provides the distance among essential
nodes.
The performance
The studies of protein-protein interactions will be more
powerful whe n the interactome co verage increases. How-
ever, the complexity of the network will also increase,
that always hampers computation tasks. After the optimi-
zation on the programs, cytoHubba is able to complete all
eleven analysis of a small network (e.g. 330 nodes, 360
edges), a mid dle size one (7,600 nodes, 20,000 edges) and
a large set (11,500 nodes, 33,600 edges) in few seconds,
around 30 seco nds a nd fe w minute s, respe ctively, on a
common desktop/ notebook (Cytoscape version 2.6.x /
2.7.x / 2.8.x on Window 7/8 platform; hardware spec as
Intel i7, 8 GB of RAM). CytoHubba has been updated
several times since 2009 (from v1.0 to v1.6). It is freely
accessible in Cyt oscape App store (http://apps.cyt oscape.
org/apps/cytohubba). The accumul ated dow nloading
number is around 6,500 (http://chianti.ucsd.edu/cyto_-
web/plugins/plugindownloadstatistics.php, statistics on
May 2014). And it is used widely to analyze cancer meta-
bolic network[6], innate immune network[7], complex
biofilm communities[8] and so on.
Validation by Predict yeast essential proteins
We use cytoHubba to score all proteins in the yeast pro -
tein interaction network by the e leven methods. DIP
database (http://dip.do e-mbi.ucla.edu, version: 20140117)
is composed of 4,908 proteins and 21,732 interactions
after removing self-interactions and redundant records.
The essential protein lists are collected from Saccharo-
myces Genome Deletion Project (SGDP) and Saccharo-
myces Genome Databa se (SGD). There are 1,122 and
1,280 proteins defined as essential pr oteins by SGDP and
SGD respectively. We use the union set (1,297 proteins)
for verifying the performance of the predictions.
The statistics of yeast PPIs are shown in Table 1. Twenty
three percent (=1148/4908) of the proteins in this network
are defined as esse ntial proteins in the dataset. We call a
nodeishigh-degreeifthenumberofitsneighborsisgreater
than a threshold; otherwise we call it a low-degree node,
where the threshold is the maximum integer such that
2 ×
vV,De
g
(v)>t
Deg(v) >
vV
Deg(v
)
.Thethreshold
of the PPI network (DIP 20140117) used in this paper is 21.
As shown as Table 1, there are 4,396 proteins in low-degree
category and 512 proteins in high-degree category, in which
908proteinsand214proteins are essential proteins.
Table 2 shows the performance (precision of predic-
tion) to predict essential proteins in top × ranked node
identified by each method. In most methods, the preci-
sion decrea ses w hen the selected n umber increase.
Besides, a local-based method is better than global-
based method in discovering yeast essential proteins. To
further understand the preference of network feature
selected by different methods, we compare the number
of proteins in common in the top 100 ranked of any
two scoring methods (Table 3). The top 100 ranked list
of Closeness is most identical to the result of Radiality
(99%), indicate that the network features detected by
these two methods are very similar. MCC shares less
common components to oth er methods (less than 30%).
The top 100 ranked proteins suggested by DMNC do
not appear in other methods list except MCC (30%),
means this method detect different features from the
other ten methods.
As shown as Table 1, in the y east PPIs, 21% of pro-
teins in low -degree cat egory are essential prote ins and
42% of proteins are essential proteins in high-degree
category. Accordingly, if we pick a protein randomly
from the high-degree pool, the probability that an essen-
tial protein being chosen is 0.42, an d 0.21 from a low-
degree protein p ool respectively. Table 4 is the number
of essential proteins found in the top × list with low-
degree feature. Fo r example, among the top 30 protein
ranked by DMNC, 29 proteins are in the l ow-degree
category, in which 21 out of 29 proteins are essential
proteins. Methods except DMNC, MCC and EcCentri-
city tend to assign higher scores to a node when it owns
more neighbors while almost no any low-degree pro-
teins are found in their top × list. In other word, these
methods cannot find low-degree essential proteins.
Conclusions
In this study, we im plement o ur network scoring meth-
ods, MCC, MNC and DMNC, and eight other popular
Table 1 Statistics of Yeast PPIs used in this study (DIP
database, 20140117 released set), in the aspects of
degree and essentiality
Total Low-degree High-degree
The number of proteins 4908 4396 512
Essential proteins (%) 1148 908 214
(23%) (21%) (42%)
Chin et al. BMC Systems Biology 2014, 8(Suppl 4):S11
http://www.biomedcentral.com/1752-0509/8/S4/S11
Page 3 of 7

资源文件列表:

graphQL-main.zip 大约有735个文件
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Delphi IDE 包提供即时代码分析和 linting
所有算法均用 Rust 实现
项目结构 该项目组织如下: src/ my_algo_category/ mod.rs my_algorithm.rs some_other_algorithm.rs some_other_algo_category/ ... mod.rs包含导出: mod my_algorithm; pub use self::my_algorithm::my_algorithm; my_algorithm.rs包含您的算法和相关测试: pub fn my_algorithm() { // ... } #[cfg(test)] mod tests { #[test] fn my_test() { // ... } }
Fortran标准库实用程序
Fortran 标准库的目标是实现以下总体范围: 实用程序(容器、字符串、文件、操作系统/环境集成、单元测试和断言、日志记录......) 算法(搜索和排序,合并......) 数学(线性代数、稀疏矩阵、特殊函数、快速傅里叶变换、随机数、统计学、常微分方程、数值积分、最优化……)
用于为 MATLAB simulink C 调用程序生成 PWM 信号的 C 代码
用于为 MATLAB simulink C 调用程序生成 PWM 信号的 C 代码