Parcourir par sujet "Markov chain"
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The complex evolutionary history of aminoacyl-tRNA synthetases
(2017)Aminoacyl-tRNA synthetases (AARSs) are a superfamily of enzymes responsible for the faithful translation of the genetic code and have lately become a prominent target for synthetic biologists. Our largescale analysis of > ... -
Evaluation of the sensitivity and specificity of three diagnostic tests for Coxiella burnetii infection in cattle and buffaloes in Punjab (India) using Bayesian latent class analysis
(2022)Q Fever is a zoonotic disease of significant animal and public health concern, caused by Coxiella burnetii (C. burnetii), an obligate intracellular bacterium. This study was done to evaluate the diagnostic sensitivity (DSe) ... -
Extending hidden Markov models to allow conditioning on previous observations
(2018)Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only ... -
Methods of analysis and meta-analysis for identifying differentially expressed genes
(2018)Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental ... -
NAT/NCS2-hound: a webserver for the detection and evolutionary classification of prokaryotic and eukaryotic nucleobase-cation symporters of the NAT/NCS2 family
(2018)Nucleobase transporters are important for supplying the cell with purines and/or pyrimidines, for controlling the intracellular pool of nucleotides, and for obtaining exogenous nitrogen/carbon sources for metabolism. ... -
PRED-TMBB2: Improved topology prediction and detection of beta-barrel outer membrane proteins
(2016)Motivation: The PRED-TMBB method is based on Hidden Markov Models and is capable of predicting the topology of beta-barrel outer membrane proteins and discriminate them from water-soluble ones. Here, we present an updated ... -
Predicting alpha helical transmembrane proteins using HMMs
(2017)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 ... -
Predicting beta barrel transmembrane proteins using HMMs
(2017)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 ... -
Semi-supervised learning of hidden markov models for biological sequence analysis
(2019)Motivation: Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are ... -
A variable parameter Shewhart control scheme for joint monitoring of process mean and variance
(2012)This paper presents a new Statistical Process Control model for the economic optimization of a variable-parameter control chart monitoring a process operation where two assignable causes may occur, one affecting the mean ...