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Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models [article]

Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan, Gert Kiss,, Vijay S. Pande
2014 arXiv   pre-print
Our approach uses L1-regularized, reversible hidden Markov models to understand large protein datasets generated via molecular dynamics simulations.  ...  We present a machine learning framework for modeling protein dynamics.  ...  While the L 1 -regularized reversible hidden Markov model represents an improvement over previous methods for analyzing MD datasets, future work will likely confront a number of remaining challenges.  ... 
arXiv:1405.1444v1 fatcat:xcg7leuht5h2rln5r24zv7lkti

Network estimation in State Space Models with L1-regularization constraint
English

Anani Lotsi, Ernst Wit
2017 Afrika Statistika  
Microarray technologies and related methods coupled with appropriate mathematical and statistical models have made it possible to identify dynamic regulatory networks by measuring time course expression  ...  However one of the challenges is the high-dimensional nature of such data coupled with the fact that these gene expression data are known not to include various biological process.  ...  The model assumes that the evolution of the hidden variables θ t is governed by the state dynamics which follow an input dependent first-order Markov process.  ... 
doi:10.16929/as/1253.103 fatcat:rcjz5cd67fcalncbewk6qlxjea

Network estimation in State Space Models with L1-regularization constraint
English

Anani Lotsi, Ernst Wit
2017 Afrika Statistika  
Microarray technologies and related methods coupled with appropriate mathematical and statistical models have made it possible to identify dynamic regulatory networks by measuring time course expression  ...  Microarray technologies and related methods coupled with appropriate mathematical and statistical models have made it possible to identify dynamic regulatory networks by measuring time course expression  ...  The model assumes that the evolution of the hidden variables θ t is governed by the state dynamics which follow an input dependent first-order Markov process.  ... 
doi:10.16929/as/2017.1253.103 fatcat:ueeuoonh4zbl5h3b2wyjlcsrli

Network estimation in State Space Model with L1-regularization constraint [article]

Anani Lotsi, Ernst Wit
2013 arXiv   pre-print
We build an input-dependent linear state space model from these hidden states and demonstrate how an incorporated L_1 regularization constraint in an Expectation-Maximization (EM) algorithm can be used  ...  Microarray technologies coupled with appropriate mathematical or statistical models have made it possible to identify dynamic regulatory networks or to measure time course of the expression level of many  ...  The model assumes that the evolution of the hidden variables θ t is governed by the state dynamics which follows an input dependent first-order Markov process.  ... 
arXiv:1308.4079v1 fatcat:pimmojzzszhhpkewvs35kzye5m

HMM'S INTERPOLATION OF PROTIENS FOR PROFILE ANALYSIS

Er. Neeshu Sharma, Er. Dinesh Kumar, Er. Reet Kamal Kaur
2011 Zenodo  
HIDDEN MARKOV MODEL (HMM) Hidden Markov models are sophisticated and flexible statistical tool for the study of protein models.  ...  Hidden Markov models (HMMs) offer a more systematic approach to estimating model parameters. The HMM is a dynamic kind of statistical profile.  ... 
doi:10.5281/zenodo.3550482 fatcat:kvqnwt44mffafcbja3lnhez6y4

Computational dynamic approaches for temporal omics data with applications to systems medicine

Yulan Liang, Arpad Kelemen
2017 BioData Mining  
Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology.  ...  This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine.  ...  State space model (dynamic linear models) and hidden Markov model are two important applications of statistical models combined with stochastic process techniques.  ... 
doi:10.1186/s13040-017-0140-x pmid:28638442 pmcid:PMC5473988 fatcat:rscvtjlpgrf53fbwlt6t4i22em

A plausible accelerating function of intermediate states in cancer metastasis

Hanah Goetz, Juan R. Melendez-Alvarez, Luonan Chen, Xiao-Jun Tian, Qing Nie
2020 PLoS Computational Biology  
First, by fitting a hidden Markov model of EMT with experimental data, we propose a statistical mechanism for EMT in which many unobservable microstates may exist within one of the observable macrostates  ...  This work advances our understanding of the dynamics and functions of EMT plasticity during cancer metastasis.  ...  Here, we use a hidden Markov model to describe the EMT process.  ... 
doi:10.1371/journal.pcbi.1007682 pmid:32155144 fatcat:uglcj2eybrdkxhgtbmzhjl6bhu

Structural Alphabets for Protein Structure Classification: A Comparison Study

Quan Le, Gianluca Pollastri, Patrice Koehl
2009 Journal of Molecular Biology  
We show that the global structural sequences approximate well the native structures of proteins, with an average coordinate root mean square of 0.69 A over 2225 test proteins.  ...  based on our fragment library) with different classifiers in their ability to classify proteins that belong to five distinct folds of CATH.  ...  sequence is given by (9) Hidden Markov Models-Hidden Markov Models (HMMs) are statistical models for modeling sequential data; they have been used successfully in pattern recognition, speech processing  ... 
doi:10.1016/j.jmb.2008.12.044 pmid:19135454 pmcid:PMC2772874 fatcat:iokrsbtbonez7ljqpdce5layya

An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

2020 International journal of recent technology and engineering  
With a voice as a bio-metric through use and significance, speech has become an important part of speech development.  ...  But the idea of machine learning and various methods are necessary for the recognition of speech in the matter of interaction with machines.  ...  Hidden Markov Model (HMM) 2. Dynamic Text Warping (DTW) 3.  ... 
doi:10.35940/ijrte.e5715.018520 fatcat:6s2iovesz5bw5kzwer4s2ubaru

Conditional Graphical Models for Protein Structural Motif Recognition

Yan Liu, Jaime Carbonell, Vanathi Gopalakrishnan, Peter Weigele
2009 Journal of Computational Biology  
These cases necessitate a more expressive model to capture the structural properties of proteins, and therefore developing a family of such predictive models is the core of this dissertation.  ...  Specifically, given a protein sequence, our goal is to predict its secondary structure elements, how they arrange themselves in three-dimensional space, and how multiple chains associate with each other  ...  Profile hidden-Markov model Profile hidden-Markov model is a Markov chain model with position specific parameterizations of emission probabilities (Durbin et al., 1998) .  ... 
doi:10.1089/cmb.2008.0176 pmid:19432536 fatcat:5nvm67fljfebxp7p2gbxmb4yim

Stem cell differentiation is a stochastic process with memory [article]

Patrick S. Stumpf, Rosanna C. G. Smith, Michael Lenz, Andreas Schuppert, Franz-Josef Müller, Ann Babtie, Thalia E. Chan, Michael P. H. Stumpf, Colin P. Please, Sam D. Howison, Fumio Arai, Ben D. MacArthur
2017 bioRxiv   pre-print
However, analysis of rate at which individual cells enter and exit this intermediate metastable state using a hidden Markov model reveals that the observed ESC and epiblast-like 'macrostates' conceal a  ...  Here, using a combination of single cell profiling and mathematical modeling, we examine the differentiation dynamics of individual mouse embryonic stem cells (ESCs) as they progress from the ground state  ...  We found that our hidden Markov model also described these dynamics remarkably well ( Fig. 4b) , suggesting that it provides a general framework to understand mammalian stem cell dynamics.  ... 
doi:10.1101/101048 fatcat:dcka2zqsvfa7vpvnt4ln7uka6e

Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks [article]

Yohann de Castro
2021 arXiv   pre-print
We introduce Markov Random Geometric Graphs (MRGGs), a growth model for temporal dynamic networks.  ...  It is based on a Markovian latent space dynamic: consecutive latent points are sampled on the Euclidean Sphere using an unknown Markov kernel; and two nodes are connected with a probability depending on  ...  Invariant distribution and reversibility for the Markov chain Reversibility of the Markov chain (X i ) i≥1 . Lemma 1 For all x, z ∈ S d−1 , P (x, z) = P (z, x) = P (e d , R e d z x).  ... 
arXiv:2006.07001v2 fatcat:ltuuw4s44jbsfbq67yoejm7wpm

Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate

Gabriel Krouk, Piotr Mirowski, Yann LeCun, Dennis E Shasha, Gloria M Coruzzi
2010 Genome Biology  
The experiments and methods allow us to propose a temporal working model for nitrate-driven gene networks. This network model is tested both in silico and experimentally.  ...  A state-space model inferred from this microarray time-series data successfully predicts gene behavior in unlearnt conditions.  ...  Modeling tools are developed under NSF DBI-0445666 to GC and DS. GK is supported by a European-FP7-International Outgoing Fellowship (Marie Curie) (AtSYSTM-BIOL; PIOF-GA-2008-220157).  ... 
doi:10.1186/gb-2010-11-12-r123 pmid:21182762 pmcid:PMC3046483 fatcat:klzc476yczeypin2p34uu32fue

Machine Learning Approaches for Metalloproteins

Yue Yu, Ruobing Wang, Ruijie D. Teo
2022 Molecules  
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties.  ...  Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery.  ...  Unsupervised learning is used for clustering and association (i.e., using hidden Markov models (HMMs) [16] ).  ... 
doi:10.3390/molecules27041277 pmid:35209064 pmcid:PMC8878495 fatcat:htlft2i7ifcwjbfzrza4ubqela

A Survey on Recurrent Neural Network Based Modelling of Gene Regulatory Network

Sudip Mandal
2016 MOJ Proteomics & Bioinformatics  
Among the different popular models to infer GRN, Recurrent Neural Networks (RNN) are considered as most popular and promising mathematical tool to model the dynamics of, as well as to infer the correct  ...  With availability of large dimensional microarray data, relationships among thousands of genes can be extracted simultaneously that is a reverse engineering problem.  ...  between network states that merge the features of Hidden Markov model to include the feedback.  ... 
doi:10.15406/mojpb.2016.04.00125 fatcat:htwayj2gvvchjboobxnbqi5tye
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