6,848 research outputs found
Flexible protein folding by ant colony optimization
Protein structure prediction is one of the most challenging topics in bioinformatics.
As the protein structure is found to be closely related to its functions,
predicting the folding structure of a protein to judge its functions is meaningful to
the humanity. This chapter proposes a flexible ant colony (FAC) algorithm for solving
protein folding problems (PFPs) based on the hydrophobic-polar (HP) square lattice
model. Different from the previous ant algorithms for PFPs, the pheromones in the
proposed algorithm are placed on the arcs connecting adjacent squares in the lattice.
Such pheromone placement model is similar to the one used in the traveling salesmen
problems (TSPs), where pheromones are released on the arcs connecting the cities.
Moreover, the collaboration of effective heuristic and pheromone strategies greatly
enhances the performance of the algorithm so that the algorithm can achieve good
results without local search methods. By testing some benchmark two-dimensional
hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed
algorithm is quite competitive compared with some other well-known methods
for solving the same protein folding problems
A Molecular Biology Database Digest
Computational Biology or Bioinformatics has been defined as the application of mathematical
and Computer Science methods to solving problems in Molecular Biology that require large scale
data, computation, and analysis [18]. As expected, Molecular Biology databases play an essential
role in Computational Biology research and development. This paper introduces into current
Molecular Biology databases, stressing data modeling, data acquisition, data retrieval, and the
integration of Molecular Biology data from different sources. This paper is primarily intended
for an audience of computer scientists with a limited background in Biology
XML in Motion from Genome to Drug
Information technology (IT) has emerged as a central to the solution of contemporary genomics and drug discovery problems. Researchers involved in genomics, proteomics, transcriptional profiling, high throughput structure determination, and in other sub-disciplines of bioinformatics have direct impact on this IT revolution. As the full genome sequences of many species, data from structural genomics, micro-arrays, and proteomics became available, integration of these data to a common platform require sophisticated bioinformatics tools. Organizing these data into knowledgeable databases and developing appropriate software tools for analyzing the same are going to be major challenges. XML (eXtensible Markup Language) forms the backbone of biological data representation and exchange over the internet, enabling researchers to aggregate data from various heterogeneous data resources. The present article covers a comprehensive idea of the integration of XML on particular type of biological databases mainly dealing with sequence-structure-function relationship and its application towards drug discovery. This e-medical science approach should be applied to other scientific domains and the latest trend in semantic web applications is also highlighted
Supervised cross-modal factor analysis for multiple modal data classification
In this paper we study the problem of learning from multiple modal data for
purpose of document classification. In this problem, each document is composed
two different modals of data, i.e., an image and a text. Cross-modal factor
analysis (CFA) has been proposed to project the two different modals of data to
a shared data space, so that the classification of a image or a text can be
performed directly in this space. A disadvantage of CFA is that it has ignored
the supervision information. In this paper, we improve CFA by incorporating the
supervision information to represent and classify both image and text modals of
documents. We project both image and text data to a shared data space by factor
analysis, and then train a class label predictor in the shared space to use the
class label information. The factor analysis parameter and the predictor
parameter are learned jointly by solving one single objective function. With
this objective function, we minimize the distance between the projections of
image and text of the same document, and the classification error of the
projection measured by hinge loss function. The objective function is optimized
by an alternate optimization strategy in an iterative algorithm. Experiments in
two different multiple modal document data sets show the advantage of the
proposed algorithm over other CFA methods
ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval
<p>Abstract</p> <p>Background</p> <p>The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.</p> <p>Results</p> <p>In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure <it>d<sub>ij </sub></it>by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (<it>i</it>, <it>j</it>), if their context <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i1"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>i</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> and <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i2"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>j</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing <it>d<sub>ij </sub></it>by a factor learned from the context <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i3"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>i</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> and <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i4"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>j</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula>.</p> <p>Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new <b>S</b>upervised learned <b>Dis</b>similarity measure, we update the <b>Pro</b>tein <b>H</b>ierarchial <b>Cont</b>ext <b>C</b>oherently in an iterative algorithm--<b>ProDis-ContSHC</b>.</p> <p>We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.</p> <p>Conclusions</p> <p>Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature.</p
- …