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Hidden markov

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https://www.readbyqxmd.com/read/28645261/genome-wide-analysis-of-udp-glycosyltransferase-super-family-in-brassica-rapa-and-brassica-oleracea-reveals-its-evolutionary-history-and-functional-characterization
#1
Jingyin Yu, Fan Hu, Komivi Dossa, Zhaokai Wang, Tao Ke
BACKGROUND: Glycosyltransferases comprise a highly divergent and polyphyletic multigene family that is involved in widespread modification of plant secondary metabolites in a process called glycosylation. According to conserved domains identified in their amino acid sequences, these glycosyltransferases can be classified into a single UDP-glycosyltransferase (UGT) 1 superfamily. RESULTS: We performed genome-wide comparative analysis of UGT genes to trace evolutionary history in algae, bryophytes, pteridophytes, and angiosperms; then, we further investigated the expansion mechanisms and function characterization of UGT gene families in Brassica rapa and Brassica oleracea...
June 23, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28641078/signal-dimensionality-and-the-emergence-of-combinatorial-structure
#2
Hannah Little, Kerem Eryılmaz, Bart de Boer
In language, a small number of meaningless building blocks can be combined into an unlimited set of meaningful utterances. This is known as combinatorial structure. One hypothesis for the initial emergence of combinatorial structure in language is that recombining elements of signals solves the problem of overcrowding in a signal space. Another hypothesis is that iconicity may impede the emergence of combinatorial structure. However, how these two hypotheses relate to each other is not often discussed. In this paper, we explore how signal space dimensionality relates to both overcrowding in the signal space and iconicity...
June 19, 2017: Cognition
https://www.readbyqxmd.com/read/28630425/reconstructing-cell-cycle-pseudo-time-series-via-single-cell-transcriptome-data
#3
Zehua Liu, Huazhe Lou, Kaikun Xie, Hao Wang, Ning Chen, Oscar M Aparicio, Michael Q Zhang, Rui Jiang, Ting Chen
Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets...
June 19, 2017: Nature Communications
https://www.readbyqxmd.com/read/28625764/genome-wide-identification-and-expression-profiling-of-eil-gene-family-in-woody-plant-representative-poplar-populus-trichocarpa
#4
Ertugrul Filiz, Recep Vatansever, Ibrahim Ilker Ozyigit, Mehmet Emin Uras, Ugur Sen, Naser A Anjum, Eduarda Pereira
This study aimed to improve current understanding on ethylene-insensitive 3-like (EIL) members, least explored in woody plants such as poplar (Populus trichocarpa Torr. & Grey). Herein, seven putative EIL members were identified in P. trichocarpa genome and were roughly annotated either as EIN3-like sequence associated with ethylene pathway or EIL3-like sequences related with sulfur (S)-pathway. Motif-distribution pattern of proteins also corroborated this annotation. They were distributed on six chromosomes (chr1, 3, 4 and 8-10), and were revealed to encode a protein of 509-662 residues with nuclear localization...
June 15, 2017: Archives of Biochemistry and Biophysics
https://www.readbyqxmd.com/read/28606054/variable-order-sequence-modeling-improves-bacterial-strain-discrimination-for-ion-torrent-dna-reads
#5
Thomas M Poulsen, Martin Frith
BACKGROUND: Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and classification of sequencing data is crucial. Standard mapping methods crudely treat bases as independent from their neighbors. Accuracy might be improved by using higher order paired hidden Markov models (HMMs), which model neighbor effects, but introduce design and implementation issues that have typically made them impractical for read mapping applications...
June 12, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28603402/hidden-parameter-markov-decision-processes-a-semiparametric-regression-approach-for-discovering-latent-task-parametrizations
#6
Finale Doshi-Velez, George Konidaris
Control applications often feature tasks with similar, but not identical, dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors, and introduce a semiparametric regression approach for learning its structure from data. We show that a learned HiP-MDP rapidly identifies the dynamics of new task instances in several settings, flexibly adapting to task variation.
July 2016: IJCAI: Proceedings of the Conference
https://www.readbyqxmd.com/read/28587299/remote-sensing-image-change-detection-based-on-nsct-hmt-model-and-its-application
#7
Pengyun Chen, Yichen Zhang, Zhenhong Jia, Jie Yang, Nikola Kasabov
Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information's relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images...
June 6, 2017: Sensors
https://www.readbyqxmd.com/read/28587095/comparative-study-of-lectin-domains-in-model-species-new-insights-into-evolutionary-dynamics
#8
Sofie Van Holle, Kristof De Schutter, Lore Eggermont, Mariya Tsaneva, Liuyi Dang, Els J M Van Damme
Lectins are present throughout the plant kingdom and are reported to be involved in diverse biological processes. In this study, we provide a comparative analysis of the lectin families from model species in a phylogenetic framework. The analysis focuses on the different plant lectin domains identified in five representative core angiosperm genomes (Arabidopsisthaliana, Glycine max, Cucumis sativus, Oryza sativa ssp. japonica and Oryza sativa ssp. indica). The genomes were screened for genes encoding lectin domains using a combination of Basic Local Alignment Search Tool (BLAST), hidden Markov models, and InterProScan analysis...
May 25, 2017: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28538149/icon-an-adaptation-of-infinite-hmms-for-time-traces-with-drift
#9
Ioannis Sgouralis, Steve Pressé
Bayesian nonparametric methods have recently transformed emerging areas within data science. One such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM that itself has become a workhorse in single molecule data analysis. The iHMM goes beyond the HMM by self-consistently learning all parameters learned by the HMM in addition to learning the number of states without recourse to any model selection steps. Despite its generality, simple features (such as drift), common to single molecule time traces, result in an overinterpretation of drift and the introduction of artifact states...
May 23, 2017: Biophysical Journal
https://www.readbyqxmd.com/read/28538142/an-introduction-to-infinite-hmms-for-single-molecule-data-analysis
#10
REVIEW
Ioannis Sgouralis, Steve Pressé
The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM...
May 23, 2017: Biophysical Journal
https://www.readbyqxmd.com/read/28537755/a-pilot-study-of-noninvasive-prenatal-diagnosis-of-alpha-and-beta-thalassemia-with-target-capture-sequencing-of-cell-free-fetal-dna-in-maternal-blood
#11
Wenjuan Wang, Yuan Yuan, Haiqing Zheng, Yaoshen Wang, Dan Zeng, Yihua Yang, Xin Yi, Yang Xia, Chunjiang Zhu
AIMS: Thalassemia is a dangerous hematolytic genetic disease. In south China, ∼24% Chinese carry alpha-thalassemia or beta-thalassemia gene mutations. Given the fact that the invasive sampling procedures can only be performed by professionals in experienced centers, it may increase the risk of miscarriage or infection. Thus, most people are worried about the invasive operation. As such, a noninvasive and accurate prenatal diagnosis is needed for appropriate genetic counseling for families with high risks...
May 24, 2017: Genetic Testing and Molecular Biomarkers
https://www.readbyqxmd.com/read/28532384/in-silico-approach-to-designing-rational-metagenomic-libraries-for-functional-studies
#12
Anna Kusnezowa, Lars I Leichert
BACKGROUND: With the development of Next Generation Sequencing technologies, the number of predicted proteins from entire (meta-) genomes has risen exponentially. While for some of these sequences protein functions can be inferred from homology, an experimental characterization is still a requirement for the determination of protein function. However, functional characterization of proteins cannot keep pace with our capabilities to generate more and more sequence data. RESULTS: Here, we present an approach to reduce the number of proteins from entire (meta-) genomes to a reasonably small number for further experimental characterization without loss of important information...
May 22, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28524088/a-hybrid-generalized-hidden-markov-model-based-condition-monitoring-approach-for-rolling-bearings
#13
Jie Liu, Youmin Hu, Bo Wu, Yan Wang, Fengyun Xie
The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD)...
May 18, 2017: Sensors
https://www.readbyqxmd.com/read/28522725/draft-genome-sequences-of-five-enterococcus-species-isolated-from-the-gut-of-patients-with-suspected-clostridium-difficile-infection
#14
Eduardo Castro-Nallar, Sandro L Valenzuela, Sebastián Baquedano, Carolina Sánchez, Fabiola Fernández, Annette N Trombert
We present draft genome sequences of five Enterococcus species from patients suspected of Clostridium difficile infection. Genome completeness was confirmed by presence of bacterial orthologs (97%). Gene searches using Hidden-Markov models revealed that the isolates harbor between seven and 11 genes involved in antibiotic resistance to tetracyclines, beta-lactams, and vancomycin.
May 18, 2017: Genome Announcements
https://www.readbyqxmd.com/read/28506839/evolutionary-relationships-among-protein-lysine-deacetylases-of-parasites-causing-neglected-diseases
#15
Larissa L S Scholte, Marina M Mourão, Fabiano Sviatopolk-Mirsky Pais, Jelena Melesina, Dina Robaa, Angela C Volpini, Wolfgang Sippl, Raymond J Pierce, Guilherme Oliveira, Laila A Nahum
The availability of the genomic data of diverse parasites provides an opportunity to identify new drug candidates against neglected tropical diseases affecting people worldwide. Histone modifying enzymes (HMEs) are potential candidates since they play key roles in the regulation of chromatin modifications, thus globally regulating gene expression. Furthermore, aberrant epigenetic states are often associated with human diseases, leading to great interest in HMEs as therapeutic targets. Our work focused on two families of protein lysine deacetylases (HDACs and sirtuins)...
May 13, 2017: Infection, Genetics and Evolution
https://www.readbyqxmd.com/read/28505138/estimating-density-and-temperature-dependence-of-juvenile-vital-rates-using-a-hidden-markov-model
#16
Robert M McElderry
Organisms in the wild have cryptic life stages that are sensitive to changing environmental conditions and can be difficult to survey. In this study, I used mark-recapture methods to repeatedly survey Anaea aidea (Nymphalidae) caterpillars in nature, then modeled caterpillar demography as a hidden Markov process to assess if temporal variability in temperature and density influence the survival and growth of A. aidea over time. Individual encounter histories result from the joint likelihood of being alive and observed in a particular stage, and I have included hidden states by separating demography and observations into parallel and independent processes...
May 15, 2017: Insects
https://www.readbyqxmd.com/read/28504948/substructural-regularization-with-data-sensitive-granularity-for-sequence-transfer-learning
#17
Shichang Sun, Hongbo Liu, Jiana Meng, C L Philip Chen, Yu Yang
Sequence transfer learning is of interest in both academia and industry with the emergence of numerous new text domains from Twitter and other social media tools. In this paper, we put forward the data-sensitive granularity for transfer learning, and then, a novel substructural regularization transfer learning model (STLM) is proposed to preserve target domain features at substructural granularity in the light of the condition of labeled data set size. Our model is underpinned by hidden Markov model and regularization theory, where the substructural representation can be integrated as a penalty after measuring the dissimilarity of substructures between target domain and STLM with relative entropy...
May 12, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28499990/a-state-space-and-density-estimation-framework-for-sleep-staging-in-obstructive-sleep-apnea
#18
Dae Y Kang, Pamela N DeYoung, Atul Malhotra, Robert L Owens, Todd P Coleman
OBJECTIVE: Although the importance of sleep is increasingly recognized, the lack of robust and efficient algorithms hinders scalable sleep assessment in healthy persons and those with sleep disorders. Polysomnography (PSG) and visual/manual scoring remain the gold standard in sleep evaluation, but more efficient/automated systems are needed. Most previous works have demonstrated algorithms in high agreement with the gold standard in healthy/normal (HN) individuals - not those with sleep disorders...
May 8, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28497059/a-new-algorithm-for-identifying-cis-regulatory-modules-based-on-hidden-markov-model
#19
Haitao Guo, Hongwei Huo
The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28496407/unsupervised-idealization-of-ion-channel-recordings-by-minimum-description-length-application-to-human-piezo1-channels
#20
Radhakrishnan Gnanasambandam, Morten S Nielsen, Christopher Nicolai, Frederick Sachs, Johannes P Hofgaard, Jakob K Dreyer
Researchers can investigate the mechanistic and molecular basis of many physiological phenomena in cells by analyzing the fundamental properties of single ion channels. These analyses entail recording single channel currents and measuring current amplitudes and transition rates between conductance states. Since most electrophysiological recordings contain noise, the data analysis can proceed by idealizing the recordings to isolate the true currents from the noise. This de-noising can be accomplished with threshold crossing algorithms and Hidden Markov Models, but such procedures generally depend on inputs and supervision by the user, thus requiring some prior knowledge of underlying processes...
2017: Frontiers in Neuroinformatics
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