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.
We introduce a novel Cytoscape plugin cytoHubba for ranking nodes 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) based 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.
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 analysis 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.
One major task in the post-genome era is to reconstruct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes/hubs in these interactomes is a ...way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or infectious disease caused by emerging pathogens. Hub Objects Analyzer (Hubba) is a web-based service for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory. Two characteristic analysis algorithms, Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) are developed for exploring and identifying hubs/essential nodes from interactome networks. Users can submit their own interaction data in PSI format (Proteomics Standards Initiative, version 2.5 and 1.0), tab format and tab with weight values. User will get an email notification of the calculation complete in minutes or hours, depending on the size of submitted dataset. Hubba result includes a rank given by a composite index, a manifest graph of network to show the relationship amid these hubs, and links for retrieving output files. This proposed method (DMNC || MNC) can be applied to discover some unrecognized hubs from previous dataset. For example, most of the Hubba high-ranked hubs (80% in top 10 hub list, and >70% in top 40 hub list) from the yeast protein interactome data (Y2H experiment) are reported as essential proteins. Since the analysis methods of Hubba are based on topology, it can also be used on other kinds of networks to explore the essential nodes, like networks in yeast, rat, mouse and human. The website of Hubba is freely available at http://hub.iis.sinica.edu.tw/Hubba.
A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major ...drawback of applying LDA is that it may encounter the
small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results show that the new LDA process improves the performance of a face recognition system significantly.
Allergic rhinitis and asthma are common chronic allergic diseases of the respiratory tract, which are accompanied by immunoglobulin E (IgE)-mediated inflammation and the involvement of type 2 T ...helper cells, mast cells, and eosinophils.
(Berk.) Sacc is a fungal parasite on the larva of Lepidoptera. It has been considered to be a health-promoting food and, also, one of the best-known herbal remedies for the treatment of airway diseases, such as asthma and lung inflammation. In the present study, we demonstrated the antiallergic rhinitis effect of Cs-4, a water extract prepared from the mycelium culture of
(Berk) Sacc, on ovalbumin (OVA)-induced allergic rhinitis in mice and the anti-asthmatic effect of Cs-4 in a rat model of asthma. Treatment with Cs-4 suppressed the nasal symptoms induced in OVA-sensitized and challenged mice. The inhibition was associated with a reduction in IgE/OVA-IgE and interleukin (IL)-4/IL-13 levels in the nasal fluid. Cs-4 treatment also decreased airway responsiveness and ameliorated the scratching behavior in capsaicin-challenged rats. It also reduced plasma IgE levels, as well as IgE and eosinophil peroxidase levels, in the bronchoalveolar fluid. Cs-4 treatment completely suppressed the increases in IL-4, IL-5, and IL-13 levels in rat lung tissue. In conclusion, our results suggest that Cs-4 has the potential to alleviate immune hypersensitivity reactions in allergic rhinitis and asthma.
Due to the importance of protein phosphorylation in cellular control, many researches are undertaken to predict the kinase-specific phosphorylation sites. Referred to our previous work, KinasePhos ...1.0, incorporated profile hidden Markov model (HMM) with flanking residues of the kinase-specific phosphorylation sites. Herein, a new web server, KinasePhos 2.0, incorporates support vector machines (SVM) with the protein sequence profile and protein coupling pattern, which is a novel feature used for identifying phosphorylation sites. The coupling pattern XdZ denotes the amino acid coupling-pattern of amino acid types X and Z that are separated by d amino acids. The differences or quotients of coupling strength CXdZ between the positive set of phosphorylation sites and the background set of whole protein sequences from Swiss-Prot are computed to determine the number of coupling patterns for training SVM models. After the evaluation based on k-fold cross-validation and Jackknife cross-validation, the average predictive accuracy of phosphorylated serine, threonine, tyrosine and histidine are 90, 93, 88 and 93%, respectively. KinasePhos 2.0 performs better than other tools previously developed. The proposed web server is freely available at http://KinasePhos2.mbc.nctu.edu.tw/.
Hepatocellular carcinoma (HCC) is a highly malignant tumor with a poor prognosis. Treatment of HCC is complicated by the fact that the disease is often diagnosed at an advanced stage when it is no ...longer amenable to curative surgery, and current systemic chemotherapeutics are mostly inefficacious. Sirtuin 1 (SIRT1) is a class III histone deacetylase that is implicated in gene regulations and stress resistance. In this study, we found that SIRT1 is essential for the tumorigenesis of HCC. We showed that although SIRT1 was expressed at very low levels in normal livers, it was overexpressed in HCC cell lines and in a subset of HCC. Tissue microarray analysis of HCC and adjacent nontumoral liver tissues revealed a positive correlation between the expression levels of SIRT1 and advancement in tumor grades. Downregulation of SIRT1 consistently suppressed the proliferation of HCC cells via the induction of cellular senescence or apoptosis. SIRT1 silencing also caused telomere dysfunction-induced foci and nuclear abnormality that were clearly associated with reduced expressions of telomerase reverse transcriptase (TERT), and PTOP, which is a member of the shelter in complex. Ectopic expression of either TERT or PTOP in SIRT1-depleted cells significantly restored cell proliferation. There was also a positive correlation between the level of induction of SIRT1 and TERT corrected in human HCC. Finally, SIRT1-silencing sensitized HCC cells to doxorubicin treatment. Together, our findings reveal a novel function for SIRT1 in telomere maintenance of HCC, and they rationalize the clinical exploration of SIRT1 inhibitors for HCC therapy.
Many research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful information to understand how ...biological systems work. Therefore, detecting functional modules is an important research topic in the post-genome era. One of functional module detecting methods is to find dense regions in Protein-Protein Interaction (PPI) networks. Most of current methods neglect confidence-scores of interactions, and pay little attention on using gene expression data to improve their results.
In this paper, we propose a novel hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles, and we name it HUNTER. Our method not only can extract functional modules from a weighted PPI network, but also use gene expression data as optional input to increase the quality of outcomes. Using HUNTER on yeast data, we found it can discover more novel components related with RNA polymerase complex than those existed methods from yeast interactome. And these new components show the close relationship with polymerase after functional analysis on Gene Ontology.
A C++ implementation of our prediction method, dataset and supplementary material are available at http://hub.iis.sinica.edu.tw/Hunter/. Our proposed HUNTER method has been applied on yeast data, and the empirical results show that our method can accurately identify functional modules. Such useful application derived from our algorithm can reconstruct the biological machinery, identify undiscovered components and decipher common sub-modules inside these complexes like RNA polymerases I, II, III.
Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, in an important data-mining problem with broad applications. Although conventional sequential ...patterns can reveal the order of items, the time between items is not determined; that is, a sequential pattern does not include time intervals between successive items. Accordingly, this work addresses sequential patterns that include time intervals, called time-interval sequential patterns. This work develops two efficient algorithms for mining time-interval sequential patterns. The first algorithm is based on the conventional Apriori algorithm, while the second one is based on the PrefixSpan algorithm. The latter algorithm outperforms the former, not only in computing time but also in scalability with respect to various parameters.
Let
T
be a tree on a set
V
of nodes. The
p
-
th power
T
p
of
T
is the graph on
V
such that any two nodes
u
and
w
of
V
are adjacent in
T
p
if and only if the distance of
u
and
w
in
T
is at most
p
. ...Given an
n
-node
m
-edge graph
G
and a positive integer
p
, the
p
-
th tree root problem
asks for a tree
T
, if any, such that
G
=
T
p
. Given an
n
-node
m
-edge graph
G
, the
tree root problem
asks for a positive integer
p
and a tree
T
, if any, such that
G
=
T
p
. Kearney and Corneil gave the best previously known algorithms for both problems. Their algorithm for the former (respectively, latter) problem runs in
O
(
n
3
) (respectively,
O
(
n
4
)) time. In this paper, we give
O
(
n
+
m
)-time algorithms for both problems.
Given a set
S
={
S
1
,
S
2
,…,
S
l
} of
l
strings, a text
T
, and a natural number
k
, find a string
M
, which is a concatenation of
k
strings (not necessarily distinct, i.e., a string in
S
may ...occur more than once in
M
) from
S
, whose longest common subsequence with
T
is largest, where a string in
S
may occur more than once in
M
. Such a string is called a
k
-inlay. The resequencing longest common subsequence problem (resequencing LCS problem for short) is to find a
k
-inlay for each query with parameter
k
after
T
and
S
are given. In this paper, we propose an algorithm for solving this problem which takes
O
(
nml
) preprocessing time and
O
(
ϑ
k
k
) query time for each query with parameter
k
, where
n
is the length of
T
,
m
is the maximal length of strings in
S
, and
ϑ
k
is the length of the longest common subsequence between a
k
-inlay and
T
.
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