8,123 research outputs found
The EM Algorithm and the Rise of Computational Biology
In the past decade computational biology has grown from a cottage industry
with a handful of researchers to an attractive interdisciplinary field,
catching the attention and imagination of many quantitatively-minded
scientists. Of interest to us is the key role played by the EM algorithm during
this transformation. We survey the use of the EM algorithm in a few important
computational biology problems surrounding the "central dogma"; of molecular
biology: from DNA to RNA and then to proteins. Topics of this article include
sequence motif discovery, protein sequence alignment, population genetics,
evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A multi-view approach to cDNA micro-array analysis
The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research
Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences
under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China
under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany
A Factor Graph Approach to Automated GO Annotation
As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.Fil: Spetale, Flavio Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Krsticevic, Flavia Jorgelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Roda, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Bulacio, Pilar Estela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentin
Network estimation in State Space Model with L1-regularization constraint
Biological networks have arisen as an attractive paradigm of genomic science
ever since the introduction of large scale genomic technologies which carried
the promise of elucidating the relationship in functional genomics. Microarray
technologies coupled with appropriate mathematical or statistical models have
made it possible to identify dynamic regulatory networks or to measure time
course of the expression level of many genes simultaneously. However one of the
few limitations fall on the high-dimensional nature of such data coupled with
the fact that these gene expression data are known to include some hidden
process. In that regards, we are concerned with deriving a method for inferring
a sparse dynamic network in a high dimensional data setting. We assume that the
observations are noisy measurements of gene expression in the form of mRNAs,
whose dynamics can be described by some unknown or hidden process. We build an
input-dependent linear state space model from these hidden states and
demonstrate how an incorporated regularization constraint in an
Expectation-Maximization (EM) algorithm can be used to reverse engineer
transcriptional networks from gene expression profiling data. This corresponds
to estimating the model interaction parameters. The proposed method is
illustrated on time-course microarray data obtained from a well established
T-cell data. At the optimum tuning parameters we found genes TRAF5, JUND, CDK4,
CASP4, CD69, and C3X1 to have higher number of inwards directed connections and
FYB, CCNA2, AKT1 and CASP8 to be genes with higher number of outwards directed
connections. We recommend these genes to be object for further investigation.
Caspase 4 is also found to activate the expression of JunD which in turn
represses the cell cycle regulator CDC2.Comment: arXiv admin note: substantial text overlap with arXiv:1308.359
Stochastic Physics, Complex Systems and Biology
In complex systems, the interplay between nonlinear and stochastic dynamics,
e.g., J. Monod's necessity and chance, gives rise to an evolutionary process in
Darwinian sense, in terms of discrete jumps among attractors, with punctuated
equilibrium, spontaneous random "mutations" and "adaptations". On an
evlutionary time scale it produces sustainable diversity among individuals in a
homogeneous population rather than convergence as usually predicted by a
deterministic dynamics. The emergent discrete states in such a system, i.e.,
attractors, have natural robustness against both internal and external
perturbations. Phenotypic states of a biological cell, a mesoscopic nonlinear
stochastic open biochemical system, could be understood through such a
perspective.Comment: 10 page
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