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https://www.readbyqxmd.com/read/28226798/decoding-speech-using-the-timing-of-neural-signal-modulation
#1
Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang
Brain-machine interfaces (BMIs) have great potential for applications that restore and assist communication for paralyzed individuals. Recently, BMIs decoding speech have gained considerable attention due to their potential for high information transfer rates. In this study, we propose a novel decoding approach based on hidden Markov models (HMMs) that uses the timing of neural signal changes to decode speech. We tested the decoder's performance by predicting vowels from electrocorticographic (ECoG) data of three human subjects...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226656/scoring-sequences-of-hippocampal-activity-using-hidden-markov-models
#2
Etienne Ackermann, Caleb Kemere, Etienne Ackermann, Caleb Kemere, Etienne Ackermann, Caleb Kemere
We propose a novel sequence score to determine to what extent neural activity is consistent with trajectories through latent ensemble states - virtual place fields - in an associated environment. In particular, we show how hidden Markov models (HMMs) can be used to model and analyze sequences of neural activity, and how the resulting joint probability of an observation sequence and an underlying sequence of states naturally lead to the development of a two component sequence score in which the sequential and contextual information are decoupled...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226466/using-respiratory-signals-for-the-recognition-of-human-activities
#3
Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov, Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov, Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov
Human activity recognition through wearable sensors is becoming integral to health monitoring and other applications. Typically, human activity is captured through signals from inertial sensors, while signals from other sensors have been utilized less frequently. In this study, we explored the feasibility of classifying human activities by analyzing the temporal information of respiratory signals through hidden Markov models (HMMs). Left-to-right HMMs were trained for five activities: sedentary, walking, eating, talking, and cigarette smoking...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28224501/modeling-movement-primitives-with-hidden-markov-models-for-robotic-and-biomedical-applications
#4
Michelle Karg, Dana Kulić
Movement primitives are elementary motion units and can be combined sequentially or simultaneously to compose more complex movement sequences. A movement primitive timeseries consist of a sequence of motion phases. This progression through a set of motion phases can be modeled by Hidden Markov Models (HMMs). HMMs are stochastic processes that model time series data as the evolution of a hidden state variable through a discrete set of possible values, where each state value is associated with an observation (emission) probability...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224499/finding-rna-protein-interaction-sites-using-hmms
#5
Tao Wang, Jonghyun Yun, Yang Xie, Guanghua Xiao
RNA-binding proteins play important roles in the various stages of RNA maturation through binding to its target RNAs. Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Several Hidden Markov model-based (HMM) approaches have been suggested to identify protein-RNA binding sites from CLIP-Seq datasets. In this chapter, we describe how HMM can be applied to analyze CLIP-Seq datasets, including the bioinformatics preprocessing steps to extract count information from the sequencing data before HMM and the downstream analysis steps following peak-calling...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224498/differential-gene-expression-dex-and-alternative-splicing-events-ase-for-temporal-dynamic-processes-using-hmms-and-hierarchical-bayesian-modeling-approaches
#6
Sunghee Oh, Seongho Song
In gene expression profile, data analysis pipeline is categorized into four levels, major downstream tasks, i.e., (1) identification of differential expression; (2) clustering co-expression patterns; (3) classification of subtypes of samples; and (4) detection of genetic regulatory networks, are performed posterior to preprocessing procedure such as normalization techniques. To be more specific, temporal dynamic gene expression data has its inherent feature, namely, two neighboring time points (previous and current state) are highly correlated with each other, compared to static expression data which samples are assumed as independent individuals...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224496/computationally-tractable-multivariate-hmm-in-genome-wide-mapping-studies
#7
Hyungwon Choi, Debashis Ghosh, Zhaohui Qin
Hidden Markov model (HMM) is widely used for modeling spatially correlated genomic data (series data). In genomics, datasets of this kind are generated from genome-wide mapping studies through high-throughput methods such as chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq). When multiple regulatory protein binding sites or related epigenetic modifications are mapped simultaneously, the correlation between data series can be incorporated into the latent variable inference in a multivariate form of HMM, potentially increasing the statistical power of signal detection...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224495/hidden-markov-models-in-bioinformatics-snv-inference-from-next-generation-sequence
#8
Jiawen Bian, Xiaobo Zhou
The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Hidden Markov models (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NGS are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNV detection capability in the regulatory regions of the genome...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224494/modelling-chip-seq-data-using-hmms
#9
Veronica Vinciotti
Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein binding sites. In this chapter, we show how hidden Markov models can be used for the analysis of data generated by ChIP-seq experiments. We show how a hidden Markov model can naturally account for spatial dependencies in the ChIP-seq data, how it can be used in the presence of data from multiple ChIP-seq experiments under the same biological condition, and how it naturally accounts for the different IP efficiencies of individual ChIP-seq experiments...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224491/predicting-alpha-helical-transmembrane-proteins-using-hmms
#10
Georgios N Tsaousis, Margarita C Theodoropoulou, Stavros J Hamodrakas, Pantelis G Bagos
Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes, is of great importance for the elucidation of their structure and function. Several methods have been applied for the prediction of the transmembrane segments and the topology of alpha helical transmembrane proteins utilizing different algorithmic techniques...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224490/predicting-beta-barrel-transmembrane-proteins-using-hmms
#11
Georgios N Tsaousis, Stavros J Hamodrakas, Pantelis G Bagos
Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224489/application-of-hidden-markov-models-in-biomolecular-simulations
#12
Saurabh Shukla, Zahra Shamsi, Alexander S Moffett, Balaji Selvam, Diwakar Shukla
Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224488/hmms-in-protein-fold-classification
#13
Christos Lampros, Costas Papaloukas, Themis Exarchos, Dimitrios I Fotiadis
The limitation of most HMMs is their inherent high dimensionality. Therefore we developed several variations of low complexity models that can be applied even to protein families with a few members. In this chapter we present these variations. All of them include the use of a hidden Markov model (HMM), with a small number of states (called reduced state-space HMM), which is trained with both amino acid sequence and secondary structure of proteins whose 3D structure is known and it is used for protein fold classification...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224487/introduction-to-hidden-markov-models-and-its-applications-in-biology
#14
M S Vijayabaskar
A number of real-world systems have common underlying patterns among them and deducing these patterns is important for us in order to understand the environment around us. These patterns in some instances are apparent upon observation while in many others especially those found in nature are well hidden. Moreover, the inherent stochasticity in these systems introduces sufficient noise that we need models capable to handling it in order to decipher the underlying pattern. Hidden Markov model (HMM) is a probabilistic model that is frequently used for studying the hidden patterns in an observed sequence or sets of observed sequences...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28185571/hippi-highly-accurate-protein-family-classification-with-ensembles-of-hmms
#15
Nam-Phuong Nguyen, Michael Nute, Siavash Mirarab, Tandy Warnow
BACKGROUND: Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. RESULTS: We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification)...
November 11, 2016: BMC Genomics
https://www.readbyqxmd.com/read/28163978/gmove-group-level-mobility-modeling-using-geo-tagged-social-media
#16
Chao Zhang, Keyang Zhang, Quan Yuan, Luming Zhang, Tim Hanratty, Jiawei Han
Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques...
August 2016: KDD: Proceedings
https://www.readbyqxmd.com/read/28149522/incorporating-periodic-variability-in-hidden-markov-models-for-animal-movement
#17
Michael Li, Benjamin M Bolker
BACKGROUND: Clustering time-series data into discrete groups can improve prediction and provide insight into the nature of underlying, unobservable states of the system. However, temporal variation in probabilities of group occupancy, or the rates at which individuals move between groups, can obscure such signals. We use finite mixture and hidden Markov models (HMMs), two standard clustering techniques, to model long-term hourly movement data from Florida panthers (Puma concolor coryi)...
2017: Movement Ecology
https://www.readbyqxmd.com/read/28135296/genome-wide-association-of-copy-number-polymorphisms-and-kidney-function
#18
Man Li, Jacob Carey, Stephen Cristiano, Katalin Susztak, Josef Coresh, Eric Boerwinkle, Wen Hong L Kao, Terri H Beaty, Anna Köttgen, Robert B Scharpf
Genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) have identified more than 50 loci associated with estimated glomerular filtration rate (eGFR), a measure of kidney function. However, significant SNPs account for a small proportion of eGFR variability. Other forms of genetic variation have not been comprehensively evaluated for association with eGFR. In this study, we assess whether changes in germline DNA copy number are associated with GFR estimated from serum creatinine, eGFRcrea...
2017: PloS One
https://www.readbyqxmd.com/read/27991565/wide-angle-spectrally-selective-perfect-absorber-by-utilizing-dispersionless-tamm-plasmon-polaritons
#19
Chun-Hua Xue, Feng Wu, Hai-Tao Jiang, Yunhui Li, Ye-Wen Zhang, Hong Chen
We theoretically investigate wide-angle spectrally selective absorber by utilizing dispersionless Tamm plasmon polaritons (TPPs) under TM polarization. TPPs are resonant tunneling effects occurring on the interface between one-dimensional photonic crystals (1DPCs) and metal slab, and their dispersion properties are essentially determined by that of 1DPCs. Our investigations show that dispersionless TPPs can be excited in 1DPCs containing hyperbolic metamaterials (HMMs) on metal substrate. Based on dispersionless TPPs, electromagnetic waves penetrate into metal substrate and are absorbed entirely by lossy metal, exhibiting a narrow-band and wide-angle perfect absorption for TM polarization...
December 19, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27908084/a-robust-automatic-birdsong-phrase-classification-a-template-based-approach
#20
Kantapon Kaewtip, Abeer Alwan, Colm O'Reilly, Charles E Taylor
Automatic phrase detection systems of bird sounds are useful in several applications as they reduce the need for manual annotations. However, birdphrase detection is challenging due to limited training data and background noise. Limited data occur because of limited recordings or the existence of rare phrases. Background noise interference occurs because of the intrinsic nature of the recording environment such as wind or other animals. This paper presents a different approach to birdsong phrase classification using template-based techniques suitable even for limited training data and noisy environments...
November 2016: Journal of the Acoustical Society of America
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