Hidden semi-Markov models
SZ Yu - Artificial intelligence, 2010 - Elsevier
As an extension to the popular hidden Markov model (HMM), a hidden semi-Markov model
(HSMM) allows the underlying stochastic process to be a semi-Markov chain. Each state has …
(HSMM) allows the underlying stochastic process to be a semi-Markov chain. Each state has …
Protein secondary structure prediction continues to rise
B Rost - Journal of structural biology, 2001 - Elsevier
Methods predicting protein secondary structure improved substantially in the 1990s through
the use of evolutionary information taken from the divergence of proteins in the same …
the use of evolutionary information taken from the divergence of proteins in the same …
[HTML][HTML] Protein secondary structure prediction using deep convolutional neural fields
Protein secondary structure (SS) prediction is important for studying protein structure and
function. When only the sequence (profile) information is used as input feature, currently the …
function. When only the sequence (profile) information is used as input feature, currently the …
Springer series in statistics
The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
[图书][B] Monte Carlo strategies in scientific computing
JS Liu, JS Liu - 2001 - Springer
This book provides a self-contained and up-to-date treatment of the Monte Carlo method
and develops a common framework under which various Monte Carlo techniques can be" …
and develops a common framework under which various Monte Carlo techniques can be" …
Bayesian methods for hidden Markov models: Recursive computing in the 21st century
SL Scott - Journal of the American statistical Association, 2002 - Taylor & Francis
Markov chain Monte Carlo (MCMC) sampling strategies can be used to simulate hidden
Markov model (HMM) parameters from their posterior distribution given observed data …
Markov model (HMM) parameters from their posterior distribution given observed data …
Prediction of 8-state protein secondary structures by a novel deep learning architecture
B Zhang, J Li, Q Lü - BMC bioinformatics, 2018 - Springer
Background Protein secondary structure can be regarded as an information bridge that links
the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction …
the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction …
Bayesian infinite mixture model based clustering of gene expression profiles
M Medvedovic, S Sivaganesan - Bioinformatics, 2002 - academic.oup.com
Motivation: The biologic significance of results obtained through cluster analyses of gene
expression data generated in microarray experiments have been demonstrated in many …
expression data generated in microarray experiments have been demonstrated in many …
Deep supervised and convolutional generative stochastic network for protein secondary structure prediction
J Zhou, O Troyanskaya - International conference on …, 2014 - proceedings.mlr.press
Predicting protein secondary structure is a fundamental problem in protein structure
prediction. Here we present a new supervised generative stochastic network (GSN) based …
prediction. Here we present a new supervised generative stochastic network (GSN) based …
Bayesian methods in bioinformatics and computational systems biology
DJ Wilkinson - Briefings in bioinformatics, 2007 - academic.oup.com
Bayesian methods are valuable, inter alia, whenever there is a need to extract information
from data that are uncertain or subject to any kind of error or noise (including measurement …
from data that are uncertain or subject to any kind of error or noise (including measurement …