3,336 research outputs found
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
Adaptive Evolutionary Clustering
In many practical applications of clustering, the objects to be clustered
evolve over time, and a clustering result is desired at each time step. In such
applications, evolutionary clustering typically outperforms traditional static
clustering by producing clustering results that reflect long-term trends while
being robust to short-term variations. Several evolutionary clustering
algorithms have recently been proposed, often by adding a temporal smoothness
penalty to the cost function of a static clustering method. In this paper, we
introduce a different approach to evolutionary clustering by accurately
tracking the time-varying proximities between objects followed by static
clustering. We present an evolutionary clustering framework that adaptively
estimates the optimal smoothing parameter using shrinkage estimation, a
statistical approach that improves a naive estimate using additional
information. The proposed framework can be used to extend a variety of static
clustering algorithms, including hierarchical, k-means, and spectral
clustering, into evolutionary clustering algorithms. Experiments on synthetic
and real data sets indicate that the proposed framework outperforms static
clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox
available at http://tbayes.eecs.umich.edu/xukevin/affec
Attributed Network Embedding for Learning in a Dynamic Environment
Network embedding leverages the node proximity manifested to learn a
low-dimensional node vector representation for each node in the network. The
learned embeddings could advance various learning tasks such as node
classification, network clustering, and link prediction. Most, if not all, of
the existing works, are overwhelmingly performed in the context of plain and
static networks. Nonetheless, in reality, network structure often evolves over
time with addition/deletion of links and nodes. Also, a vast majority of
real-world networks are associated with a rich set of node attributes, and
their attribute values are also naturally changing, with the emerging of new
content patterns and the fading of old content patterns. These changing
characteristics motivate us to seek an effective embedding representation to
capture network and attribute evolving patterns, which is of fundamental
importance for learning in a dynamic environment. To our best knowledge, we are
the first to tackle this problem with the following two challenges: (1) the
inherently correlated network and node attributes could be noisy and
incomplete, it necessitates a robust consensus representation to capture their
individual properties and correlations; (2) the embedding learning needs to be
performed in an online fashion to adapt to the changes accordingly. In this
paper, we tackle this problem by proposing a novel dynamic attributed network
embedding framework - DANE. In particular, DANE first provides an offline
method for a consensus embedding and then leverages matrix perturbation theory
to maintain the freshness of the end embedding results in an online manner. We
perform extensive experiments on both synthetic and real attributed networks to
corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page
Some studies on protein structure alignment algorithms
The alignment of two protein structures is a fundamental problem in structural bioinformatics.Their structural similarity carries with it the connotation of similar functional behavior that couldbe exploited in various applications. A plethora of algorithms, including one by us, is a testamentto the importance of the problem. In this thesis, we propose a novel approach to measure theeectiveness of a sample of four such algorithms, DALI, TM-align, CE and EDAlignsse, for de-tecting structural similarities among proteins. The underlying premise is that structural proximityshould translate into spatial proximity. To verify this, we carried out extensive experiments withve dierent datasets, each consisting of proteins from two to six dierent families.In further addition to our work, we have focused on the area of computational methods foraligning multiple protein structures. This problem is known for its np-complete nature. Therefore,there are many ways to come up with a solution which can be better than the existing ones or atleast as good as them. Such a solution is presented here in this thesis. We have used a heuristicalgorithm which is the Progressive Multiple Alignment approach, to have the multiple sequencealignment. We used the root mean square deviation (RMSD) as a measure of alignment quality andreported this measure for a large and varied number of alignments. We also compared the executiontimes of our algorithm with the well-known algorithm MUSTANG for all the tested alignments
Towards Reliable Automatic Protein Structure Alignment
A variety of methods have been proposed for structure similarity calculation,
which are called structure alignment or superposition. One major shortcoming in
current structure alignment algorithms is in their inherent design, which is
based on local structure similarity. In this work, we propose a method to
incorporate global information in obtaining optimal alignments and
superpositions. Our method, when applied to optimizing the TM-score and the GDT
score, produces significantly better results than current state-of-the-art
protein structure alignment tools. Specifically, if the highest TM-score found
by TMalign is lower than (0.6) and the highest TM-score found by one of the
tested methods is higher than (0.5), there is a probability of (42%) that
TMalign failed to find TM-scores higher than (0.5), while the same probability
is reduced to (2%) if our method is used. This could significantly improve the
accuracy of fold detection if the cutoff TM-score of (0.5) is used.
In addition, existing structure alignment algorithms focus on structure
similarity alone and simply ignore other important similarities, such as
sequence similarity. Our approach has the capacity to incorporate multiple
similarities into the scoring function. Results show that sequence similarity
aids in finding high quality protein structure alignments that are more
consistent with eye-examined alignments in HOMSTRAD. Even when structure
similarity itself fails to find alignments with any consistency with
eye-examined alignments, our method remains capable of finding alignments
highly similar to, or even identical to, eye-examined alignments.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
A New Quartet Tree Heuristic for Hierarchical Clustering
We consider the problem of constructing an an optimal-weight tree from the
3*(n choose 4) weighted quartet topologies on n objects, where optimality means
that the summed weight of the embedded quartet topologiesis optimal (so it can
be the case that the optimal tree embeds all quartets as non-optimal
topologies). We present a heuristic for reconstructing the optimal-weight tree,
and a canonical manner to derive the quartet-topology weights from a given
distance matrix. The method repeatedly transforms a bifurcating tree, with all
objects involved as leaves, achieving a monotonic approximation to the exact
single globally optimal tree. This contrasts to other heuristic search methods
from biological phylogeny, like DNAML or quartet puzzling, which, repeatedly,
incrementally construct a solution from a random order of objects, and
subsequently add agreement values.Comment: 22 pages, 14 figure
- …