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Hidden markov

Gabriel Moreno-Hagelsieb, Bennett Vitug, Arturo Medrano-Soto, Milton H Saier
The membrane attack complex/perforin (MACPF) superfamily consists of a diverse group of proteins involved in bacterial pathogenesis and sporulation as well as eukaryotic immunity, embryonic development, neural migration and fruiting body formation. The present work shows that the evolutionary relationships between the members of the superfamily, previously suggested by comparison of their tertiary structures, can also be supported by analyses of their primary structures. The superfamily includes the MACPF family (TC 1...
November 17, 2017: Journal of Molecular Microbiology and Biotechnology
Hongle Wu, Takafumi Kato, Masayuki Numao, Ken-Ichi Fukui
A good sleep is important for a healthy life. Recently, several consumer sleep devices have emerged on the market claiming that they can provide personal sleep monitoring; however, many of them require additional hardware or there is a lack of scientific evidence regarding their reliability. In this paper we proposed a novel method to assess the sleep quality through sound events recorded in the bedroom. We used subjective sleep quality as training label, combined several machine learning approaches including kernelized self organizing map, hierarchical clustering and hidden Markov model, obtained the models to indicate the sleep pattern of specific quality level...
December 2017: Health Information Science and Systems
Christine L Barnett, Gregory B Auffenberg, Zian Cheng, Fan Yang, Jiachen Wang, John T Wei, David C Miller, James E Montie, Mufaddal Mamawala, Brian T Denton
BACKGROUND: Active surveillance (AS) for prostate cancer includes follow-up with serial prostate biopsies. The optimal biopsy frequency during follow-up has not been determined. The goal of this investigation was to use longitudinal AS biopsy data to assess whether the frequency of biopsy could be reduced without substantially prolonging the time to the detection of disease with a Gleason score ≥ 7. METHODS: With data from 1375 men with low-risk prostate cancer enrolled in AS at Johns Hopkins, a hidden Markov model was developed to estimate the probability of undersampling at diagnosis, the annual probability of grade progression, and the 10-year cumulative probability of reclassification or progression to Gleason score ≥ 7...
November 13, 2017: Cancer
Christos Mousas
This paper presents a method of reconstructing full-body locomotion sequences for virtual characters in real-time, using data from a single inertial measurement unit (IMU). This process can be characterized by its difficulty because of the need to reconstruct a high number of degrees of freedom (DOFs) from a very low number of DOFs. To solve such a complex problem, the presented method is divided into several steps. The user's full-body locomotion and the IMU's data are recorded simultaneously. Then, the data is preprocessed in such a way that would be handled more efficiently...
November 10, 2017: Sensors
Jason Ernst, Manolis Kellis
Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment...
December 2017: Nature Protocols
Jihyun Kim, Thi-Thu-Huong Le, Howon Kim
Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much...
2017: Computational Intelligence and Neuroscience
Daniel H Haft, Michael DiCuccio, Azat Badretdin, Vyacheslav Brover, Vyacheslav Chetvernin, Kathleen O'Neill, Wenjun Li, Farideh Chitsaz, Myra K Derbyshire, Noreen R Gonzales, Marc Gwadz, Fu Lu, Gabriele H Marchler, James S Song, Narmada Thanki, Roxanne A Yamashita, Chanjuan Zheng, Françoise Thibaud-Nissen, Lewis Y Geer, Aron Marchler-Bauer, Kim D Pruitt
The Reference Sequence (RefSeq) project at the National Center for Biotechnology Information (NCBI) provides annotation for over 95 000 prokaryotic genomes that meet standards for sequence quality, completeness, and freedom from contamination. Genomes are annotated by a single Prokaryotic Genome Annotation Pipeline (PGAP) to provide users with a resource that is as consistent and accurate as possible. Notable recent changes include the development of a hierarchical evidence scheme, a new focus on curating annotation evidence sources, the addition and curation of protein profile hidden Markov models (HMMs), release of an updated pipeline (PGAP-4), and comprehensive re-annotation of RefSeq prokaryotic genomes...
November 3, 2017: Nucleic Acids Research
An T Nguyen, Byron C Wallace, Junyi Jessy Li, Ani Nenkova, Matthew Lease
Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory...
2017: Proceedings of the Conference on Computational Linguistics
Katie L Druce, John McBeth, Sabine N van der Veer, David A Selby, Bertie Vidgen, Konstantinos Georgatzis, Bruce Hellman, Rashmi Lakshminarayana, Afiqul Chowdhury, David M Schultz, Caroline Sanders, Jamie C Sergeant, William G Dixon
BACKGROUND: The huge increase in smartphone use heralds an enormous opportunity for epidemiology research, but there is limited evidence regarding long-term engagement and attrition in mobile health (mHealth) studies. OBJECTIVE: The objective of this study was to examine how representative the Cloudy with a Chance of Pain study population is of wider chronic-pain populations and to explore patterns of engagement among participants during the first 6 months of the study...
November 1, 2017: JMIR MHealth and UHealth
Zhiqun Xie, Haixu Tang
Motivation: The insertion sequence (IS) elements are the smallest but most abundant autonomous transposable elements in prokaryotic genomes, which play a key role in prokaryotic genome organization and evolution. With the fast growing genomic data, it is becoming increasingly critical for biology researchers to be able to accurately and automatically annotate ISs in prokaryotic genome sequences. The available automatic IS annotation systems are either providing only incomplete IS annotation or relying on the availability of existing genome annotations...
November 1, 2017: Bioinformatics
Joseph W Houpt, Mary E Frame, Leslie M Blaha
The first stage of analyzing eye-tracking data is commonly to code the data into sequences of fixations and saccades. This process is usually automated using simple, predetermined rules for classifying ranges of the time series into events, such as "if the dispersion of gaze samples is lower than a particular threshold, then code as a fixation; otherwise code as a saccade." More recent approaches incorporate additional eye-movement categories in automated parsing algorithms by using time-varying, data-driven thresholds...
October 26, 2017: Behavior Research Methods
Dinghua Li, Yukun Huang, Chi-Ming Leung, Ruibang Luo, Hing-Fung Ting, Tak-Wah Lam
BACKGROUND: The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of de Bruijn graph gives obvious advantage over other assembly methods. Xander, as a pilot study, indeed has a lot of room for improvement. Apart from its slow speed, Xander uses only 1 k-mer size for graph construction and whatever choice of k will compromise either sensitivity or accuracy. Xander uses a Bloom-filter representation of de Bruijn graph to achieve a lower memory footprint...
October 16, 2017: BMC Bioinformatics
Prapaporn Techa-Angkoon, Yanni Sun, Jikai Lei
BACKGROUND: Homology search is still a significant step in functional analysis for genomic data. Profile Hidden Markov Model-based homology search has been widely used in protein domain analysis in many different species. In particular, with the fast accumulation of transcriptomic data of non-model species and metagenomic data, profile homology search is widely adopted in integrated pipelines for functional analysis. While the state-of-the-art tool HMMER has achieved high sensitivity and accuracy in domain annotation, the sensitivity of HMMER on short reads declines rapidly...
October 16, 2017: BMC Bioinformatics
Fermín Segovia, Juan M Górriz, Javier Ramírez, Francisco J Martínez-Murcia, Diego Salas-Gonzalez
(18)F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson's disease (PD) that allows us to examine postsynaptic dopamine D2/3 receptors. Like other neuroimaging modalities used for PD diagnosis, most of the total intensity of (18)F-DMFP-PET images is concentrated in the striatum. However, other regions can also be useful for diagnostic purposes. An appropriate delimitation of the regions of interest contained in (18)F-DMFP-PET data is crucial to improve the automatic diagnosis of PD. In this manuscript we propose a novel methodology to preprocess (18)F-DMFP-PET data that improves the accuracy of computer aided diagnosis systems for PD...
2017: Frontiers in Aging Neuroscience
Simone Sulpizio, Kaori Kuroda, Matteo Dalsasso, Tetsuya Asakawa, Marc H Bornstein, Hirokazu Doi, Gianluca Esposito, Kazuyuki Shinohara
The aim of the present work was a cross-linguistic generalization of Inoue et al.'s (2011) algorithm for discriminating infant- (IDS) vs. adult-directed speech (ADS). IDS is the way in which mothers communicate with infants; it is a universal communicative property, with some cross-linguistic differences. Inoue et al. (2011) implemented a machine algorithm that, by using a mel-frequency cepstral coefficient and a hidden Markov model, discriminated IDS from ADS in Japanese. We applied the original algorithm to two other languages that are very different from Japanese - Italian and German - and then tested the algorithm on Italian and German databases of IDS and ADS...
October 20, 2017: Neuroscience Research
Sabrina I Soraya, Ting-Hui Chiang, Guo-Jing Chan, Yi-Juan Su, Chih-Wei Yi, Yu-Chee Tseng, Yu-Tai Ching
With the growth of aging population, elder care service has become an important part of the service industry of Internet of Things. Activity monitoring is one of the most important services in the field of the elderly care service. In this paper, we proposed a wearable solution to provide an activity monitoring service on elders for caregivers. The system uses wireless signals to estimate calorie burned by the walking and localization. In addition, it also uses wireless motion sensors to recognize physical activity, such as drinking and restroom activity...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Shuang Liu, Di Zhang, Jingjing Tong, Feng He, Hongzhi Qi, Lixin Zhang, Dong Ming
EEG-based emotion recognition has received increasing attention in the past few decades. The frequency components that give effective discrimination between different emotion states are subject specific. Identification of these subject-specific discriminative frequency components (DFCs) is important for the accurate classification of emotional activities. This paper investigated the potential of adaptive tracking of DFCs as an effective method for choosing the discriminative bands of EEG patterns and improving emotion recognition performance...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Raul I Ramos-Garcia, Edward Sazonov, Stephen Tiffany
Previous studies with the Personal Automatic Cigarette Tracker (PACT) wearable system have found that smoking presents a distinct temporal breathing pattern, which might be well-suited for recognition by hidden Markov models (HMMs). In this work, we explored the feasibility of using HMMs to characterize the temporal information of smoking inhalations contained in the respiratory signals such as tidal volume, airflow, and the signal from the hand-to-mouth proximity sensor. Left-to-right HMMs were built to classify smoking and non-smoking inhalations using either only the respiratory signals, or both respiratory and hand proximity signals...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Takayuki Mukaeda, Keisuke Shima
This paper proposes a novel sequential pattern recognition method enabling calculation of a posteriori probability for learned and unlearned classes. In this approach, probability density functions of unlearned classes are incorporated in a hiddenMarkov model to classify undefined classes via model parameter estimation using given learning samples. The technique can be applied to various pattern recognition problems such as motion classification with electromyogram (EMG) signals and in support for disease diagnosis...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Cheol-Hong Min
An infrastructure to record, detect and label the behavioral patterns of children with Autism Spectrum Disorder (ASD) has been developed. The system incorporates 2 different sensor platforms which are wearable and static. The wearable system is based on accelerometer which detects behavioral patterns of a subject, while the static sensors are microphones and cameras which captures the sounds, images and videos of the subjects within a room. The video also provides ground truth for wearable sensor data analysis...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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