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https://www.readbyqxmd.com/read/28538149/icon-an-adaptation-of-infinite-hmms-for-time-traces-with-drift
#1
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
#2
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/28532384/in-silico-approach-to-designing-rational-metagenomic-libraries-for-functional-studies
#3
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/28494756/solving-the-master-equation-for-indels
#4
EDITORIAL
Ian H Holmes
BACKGROUND: Despite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events. MAIN TEXT: This paper discusses progress in the area of insertion-deletion models, in view of recent work by Ezawa (BMC Bioinformatics 17:304, 2016); (BMC Bioinformatics 17:397, 2016); (BMC Bioinformatics 17:457, 2016) on the calculation of time-dependent gap length distributions in pairwise alignments, and current approaches for extending these approaches from ancestor-descendant pairs to phylogenetic trees...
May 12, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28469382/performance-of-hidden-markov-models-in-recovering-the-standard-classification-of-glycoside-hydrolases
#5
Mariana Fonseca Rossi, Beatriz Mello, Carlos G Schrago
Glycoside hydrolases (GHs) are carbohydrate-active enzymes that assist the hydrolysis of glycoside bonds of complex sugars into carbohydrates. The current standard GH family classification is available in the CAZy database, which is based on the similarities of amino acid sequences and curated semi-automatically. However, with the exponential increase in data availability from genome sequences, automated classification methods are required for the fast annotation of coding sequences. Currently, the dbCAN database offers automatic annotations of signature domains from CAZy-defined classifications using a statistical approach, the hidden Markov models (HMMs)...
2017: Evolutionary Bioinformatics Online
https://www.readbyqxmd.com/read/28466793/seqping-gene-prediction-pipeline-for-plant-genomes-using-self-training-gene-models-and-transcriptomic-data
#6
Kuang-Lim Chan, Rozana Rosli, Tatiana V Tatarinova, Michael Hogan, Mohd Firdaus-Raih, Eng-Ti Leslie Low
BACKGROUND: Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion...
January 27, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28462050/a-profile-hidden-markov-model-to-investigate-the-distribution-and-frequency-of-lanb-encoding-lantibiotic-modification-genes-in-the-human-oral-and-gut-microbiome
#7
Calum J Walsh, Caitriona M Guinane, Paul W O' Toole, Paul D Cotter
BACKGROUND: The human microbiota plays a key role in health and disease, and bacteriocins, which are small, bacterially produced, antimicrobial peptides, are likely to have an important function in the stability and dynamics of this community. Here we examined the density and distribution of the subclass I lantibiotic modification protein, LanB, in human oral and stool microbiome datasets using a specially constructed profile Hidden Markov Model (HMM). METHODS: The model was validated by correctly identifying known lanB genes in the genomes of known bacteriocin producers more effectively than other methods, while being sensitive enough to differentiate between different subclasses of lantibiotic modification proteins...
2017: PeerJ
https://www.readbyqxmd.com/read/28460141/hh-motif-de-novo-detection-of-short-linear-motifs-in-proteins-by-hidden-markov-model-comparisons
#8
Roman Prytuliak, Michael Volkmer, Markus Meier, Bianca H Habermann
Short linear motifs (SLiMs) in proteins are self-sufficient functional sequences that specify interaction sites for other molecules and thus mediate a multitude of functions. Computational, as well as experimental biological research would significantly benefit, if SLiMs in proteins could be correctly predicted de novo with high sensitivity. However, de novo SLiM prediction is a difficult computational task. When considering recall and precision, the performances of published methods indicate remaining challenges in SLiM discovery...
April 29, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28409487/scanpath-modeling-and-classification-with-hidden-markov-models
#9
Antoine Coutrot, Janet H Hsiao, Antoni B Chan
How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed that scanpath, i.e., the sequence of eye movements made by an observer exploring a visual stimulus, can be used to infer observer-related (e.g., task at hand) and stimuli-related (e.g., image semantic category) information. However, eye movements are complex signals and many of these studies rely on limited gaze descriptors and bespoke datasets. Here, we provide a turnkey method for scanpath modeling and classification...
April 13, 2017: Behavior Research Methods
https://www.readbyqxmd.com/read/28306515/decentralized-safety-concept-for-closed-loop-controlled-intensive-care
#10
Jan Kühn, Christian Brendle, André Stollenwerk, Martin Schweigler, Stefan Kowalewski, Thorsten Janisch, Rolf Rossaint, Steffen Leonhardt, Marian Walter, Rüdger Kopp
This paper presents a decentralized safety concept for networked intensive care setups, for which a decentralized network of sensors and actuators is realized by embedded microcontroller nodes. It is evaluated for up to eleven medical devices in a setup for automated acute respiratory distress syndrome (ARDS) therapy. In this contribution we highlight a blood pump supervision as exemplary safety measure, which allows a reliable bubble detection in an extracorporeal blood circulation. The approach is validated with data of animal experiments including 35 bubbles with a size between 0...
April 1, 2017: Biomedizinische Technik. Biomedical Engineering
https://www.readbyqxmd.com/read/28288333/metric-learning-for-parkinsonian-identification-from-imu-gait-measurements
#11
Fabio Cuzzolin, Michael Sapienza, Patrick Esser, Suman Saha, Miss Marloes Franssen, Johnny Collett, Helen Dawes
Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with the accuracy required in clinical trials. In this paper we propose a novel framework in which IMU gait measurement sequences sampled during a 10m walk are first encoded as hidden Markov models (HMMs) to extract their dynamics and provide a fixed-length representation...
February 27, 2017: Gait & Posture
https://www.readbyqxmd.com/read/28268618/decoding-speech-using-the-timing-of-neural-signal-modulation
#12
Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja
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/28268482/scoring-sequences-of-hippocampal-activity-using-hidden-markov-models
#13
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/28268307/using-respiratory-signals-for-the-recognition-of-human-activities
#14
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/28267626/bayesian-switching-factor-analysis-for-estimating-time-varying-functional-connectivity-in-fmri
#15
Jalil Taghia, Srikanth Ryali, Tianwen Chen, Kaustubh Supekar, Weidong Cai, Vinod Menon
There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000)...
March 4, 2017: NeuroImage
https://www.readbyqxmd.com/read/28226798/decoding-speech-using-the-timing-of-neural-signal-modulation
#16
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
#17
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
#18
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
#19
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
#20
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
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