http://www.bibsonomy.org/burst/user/marcoalvarez/MarkovBibSonomy BuRST Feed for /user/marcoalvarez/Markov2008-09-07T19:53:43+02:00http://www.bibsonomy.org/bibtex/2ab00292b13715f88ea64537208ac83c6/marcoalvarezmarcoalvarez2008-05-14T11:33:59+02:00TopologyPrediction Markov <span style="color:#555555;">E. L. <a href="http://www.bibsonomy.org/author/Sonnhammer">Sonnhammer</a> und G. <a href="http://www.bibsonomy.org/author/von Heijne">von Heijne</a> und A. <a href="http://www.bibsonomy.org/author/Krogh">Krogh</a> </span><em>International Conference on Intelligent Systems for Molecular Biology</em>(<em>1998</em>)Wed May 14 11:33:59 CEST 2008International Conference on Intelligent Systems for Molecular Biology175--182A hidden markov model for predicting transmembrane helices in protein sequences61998TopologyPrediction Markov A novel method to model and predict the location and orientation of alpha helices in membrane-spanning proteins is presented. It is based on a hidden Markov model (HMM) with an architecture that corresponds closely to the biological system. The model is cyclic with 7 types of states for helix core, helix caps on either side, loop on the cytoplasmic side, two loops for the non-cytoplasmic side, and a globular domain state in the middle of each loop. The two loop paths on the non-cytoplasmic side are used to model short and long loops separately, which corresponds biologically to the two known different membrane insertions mechanisms. The close mapping between the biological and computational states allows us to infer which parts of the model architecture are important to capture the information that encodes the membrane topology, and to gain a better understanding of the mechanisms and constraints involved. Models were estimated both by maximum likelihood and a discriminative method, and a method for reassignment of the membrane helix boundaries were developed. In a cross validated test on single sequences, our transmembrane HMM, TMHMM, correctly predicts the entire topology for 77\% of the sequences in a standard dataset of 83 proteins with known topology. The same accuracy was achieved on a larger dataset of 160 proteins. These results compare favourably with existing methods.http://www.bibsonomy.org/bibtex/2b06d05c47a51ad3f5c5deea3b87be307/marcoalvarezmarcoalvarez2008-05-14T11:33:59+02:00Markov <span style="color:#555555;">A. V. <a href="http://www.bibsonomy.org/author/Lukashin">Lukashin</a> und M. <a href="http://www.bibsonomy.org/author/Borodovsky">Borodovsky</a> </span><em>Nucleic Acids Research</em><em>26(4):1107--1115</em><em>February1998. </em>Wed May 14 11:33:59 CEST 2008Nucleic Acids ResearchFebruary41107--1115GeneMark.hmm: new solutions for gene finding261998Markov The number of completely sequenced bacterial genomes has been growing fast. There are computer methods available for finding genes but yet there is a need for more accurate algorithms. The GeneMark. hmm algorithm presented here was designed to improve the gene prediction quality in terms of finding exact gene boundaries. The idea was to embed the GeneMark models into naturally derived hidden Markov model framework with gene boundaries modeled as transitions between hidden states. We also used the specially derived ribosome binding site pattern to refine predictions of translation initiation codons. The algorithm was evaluated on several test sets including 10 complete bacterial genomes. It was shown that the new algorithm is significantly more accurate than GeneMark in exact gene prediction. Interestingly, the high gene finding accuracy was observed even in the case when Markov models of order zero, one and two were used. We present the analysis of false positive and false negative predictions with the caution that these categories are not precisely defined if the public database annotation is used as a control.http://www.bibsonomy.org/bibtex/25ac9d4ce3dd9612b4e05739ecea3ce36/marcoalvarezmarcoalvarez2008-05-14T11:33:59+02:00ImageAnnotation Markov <span style="color:#555555;">Jia <a href="http://www.bibsonomy.org/author/Li">Li</a> und James Z. <a href="http://www.bibsonomy.org/author/Wang">Wang</a> </span><em>IEEE Transactions on Pattern Analysis and Machine Intelligence</em><em>25(9):1075--1088</em><em>September2003. </em>Wed May 14 11:33:59 CEST 2008IEEE Transactions on Pattern Analysis and Machine IntelligenceSeptember91075--1088Automatic linguistic indexing of pictures by a statistical modeling approach252003ImageAnnotation Markov Automatic linguistic indexing of pictures is an important but highly challenging problem for researchers in computer vision and content-based image retrieval. In this paper, we introduce a statistical modeling approach to this problem. Categorized images are used to train a dictionary of hundreds of statistical models each representing a concept. Images of any given concept are regarded as instances of a stochastic process that characterizes the concept. To measure the extent of association between an image and the textual description of a concept, the likelihood of the occurrence of the image based on the characterizing stochastic process is computed. A high likelihood indicates a strong association. In our experimental implementation, we focus on a particular group of stochastic processes, that is, the two-dimensional multiresolution hidden Markov models (2D MHMMs). We implemented and tested our ALIP (Automatic Linguistic Indexing of Pictures) system on a photographic image database of 600 different concepts, each with about 40 training images. The system is evaluated quantitatively using more than 4,600 images outside the training database and compared with a random annotation scheme. Experiments have demonstrated the good accuracy of the system and its high potential in linguistic indexing of photographic images.http://www.bibsonomy.org/bibtex/28232fa0b8025a4fe1e6def692a246fa1/marcoalvarezmarcoalvarez2008-05-14T11:33:59+02:00TopologyPrediction Markov <span style="color:#555555;">Robel Y <a href="http://www.bibsonomy.org/author/Kahsay">Kahsay</a> und Guang <a href="http://www.bibsonomy.org/author/Gao">Gao</a> und Li <a href="http://www.bibsonomy.org/author/Liao">Liao</a> </span><em>Bioinformatics</em><em>21(9):1853--1858</em><em>May2005. </em>Wed May 14 11:33:59 CEST 2008BioinformaticsMay91853--1858An improved hidden markov model for transmembrane protein detection and topology prediction and its applications to complete genomes212005TopologyPrediction Markov MOTIVATION: Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane proteins. RESULTS: We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins. Our model uses TMHMM as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89\% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD had 84\% for topology and 89\% for locations. When utilized for identifying transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does. Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for approximately 20-30\% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans. AVAILABILITY: \url{http://liao.cis.udel.edu/website/servers/TMMOD/}http://www.bibsonomy.org/bibtex/2678f13d502aa9382aea79d8f5d4ce98c/marcoalvarezmarcoalvarez2008-05-14T11:33:59+02:00Markov ImageAnnotation <span style="color:#555555;">Arnab <a href="http://www.bibsonomy.org/author/Ghoshal">Ghoshal</a> und Pavel <a href="http://www.bibsonomy.org/author/Ircing">Ircing</a> und Sanjeev <a href="http://www.bibsonomy.org/author/Khudanpur">Khudanpur</a> </span><em>Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, </em><em>Seite544--551. </em>(<em>2005</em>)Wed May 14 11:33:59 CEST 2008Annual International ACM SIGIR Conference on Research and Development in Information Retrieval544--551Hidden Markov models for automatic annotation and content-based retrieval of images and video2005Markov ImageAnnotation This paper introduces a novel method for automatic annotation of images with keywords from a generic vocabulary of concepts or objects for the purpose of content-based image retrieval. An image, represented as sequence of feature-vectors characterizing low-level visual features such as color, texture or oriented-edges, is modeled as having been stochastically generated by a hidden Markov model, whose states represent concepts. The parameters of the model are estimated from a set of manually annotated (training) images. Each image in a large test collection is then automatically annotated with the a posteriori probability of concepts present in it. This annotation supports content-based search of the image-collection via keywords. Various aspects of model parameterization, parameter estimation, and image annotation are discussed. Empirical retrieval results are presented on two image-collections | COREL and key-frames from TRECVID. Comparisons are made with two other recently developed techniques on the same datasets.http://www.bibsonomy.org/bibtex/2807a67270a546f62bfff708ba1566444/marcoalvarezmarcoalvarez2008-05-14T11:33:59+02:00Markov TFBS <span style="color:#555555;">Kyle <a href="http://www.bibsonomy.org/author/Ellrott">Ellrott</a> und Chuhu <a href="http://www.bibsonomy.org/author/Yang">Yang</a> und Frances M. <a href="http://www.bibsonomy.org/author/Sladek">Sladek</a> und Tao <a href="http://www.bibsonomy.org/author/Jiang">Jiang</a> </span><em>Bioinformatics</em><em>18(Suppl. 2):S100--S109</em>(<em>2002</em>)Wed May 14 11:33:59 CEST 2008BioinformaticsSuppl. 2S100--S109Identifying transcription factor binding sites through markov chain optimization182002Markov TFBS Even though every cell in an organism contains the same genetic material, each cell does not express the same cohort of genes. Therefore, one of the major problems facing genomic research today is to determine not only which genes are differentially expressed and under what conditions, but also how the expression of those genes is regulated. The first step in determining differential gene expression is the binding of sequence-specific DNA binding proteins (i.e. transcription factors) to regulatory regions of the genes (i.e. promoters and enhancers). An important aspect to understanding how a given transcription factor functions is to know the entire gamut of binding sites and subsequently potential target genes that the factor may bind/regulate. In this study, we have developed a computer algorithm to scan genomic databases for transcription factor binding sites, based on a novel Markov chain optimization method, and used it to scan the human genome for sites that bind to hepatocyte nuclear factor 4 alpha (HNF4alpha). A list of 71 known HNF4alpha binding sites from the literature were used to train our Markov chain model. By looking at the window of 600 nucleotides around the transcription start site of each confirmed gene on the human genome, we identified 849 sites with varying binding potential and experimentally tested 109 of those sites for binding to HNF4alpha. Our results show that the program was very successful in identifying 77 new HNF4alpha binding sites with varying binding affinities (i.e. a 71\% success rate). Therefore, this computational method for searching genomic databases for potential transcription factor binding sites is a powerful tool for investigating mechanisms of differential gene regulation.http://www.bibsonomy.org/bibtex/272acac76377a6f2ee6afb058a333ac93/marcoalvarezmarcoalvarez2008-05-14T10:25:13+02:00Markov <span style="color:#555555;">Gill <a href="http://www.bibsonomy.org/author/Bejerano">Bejerano</a> </span><em>Bioinformatics</em><em>20(5):788--789</em>(<em>2004</em>)Wed May 14 10:25:13 CEST 2008Bioinformatics5788--789Algorithms for variable length markov chain modeling202004Markov We present a general purpose implementation of variable length Markov models. Contrary to fixed order Markov models, these models are not restricted to a predefined uniform depth. Rather, by examining the training data, a model is constructed that fits higher order Markov dependencies where such contexts exist, while using lower order Markov dependencies elsewhere. As both theoretical and experimental results show, these models are capable of capturing rich signals from a modest amount of training data, without the use of hidden states.