Phylogenetic hidden Markov models, or phylo-HMMs, are probabilistic models that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way this process changes from one site to the next. By treating molecular evolution as a combination of two Markov processes—one that operates in the dimension of space (along a genome) and one that oper...

background: myocardial infarction (mi) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. this study aimed to present an mi prediction model using classification data mining methods, which consider the imbalance nature of the problem. methods: we enrolled 455 healthy and 295 myocardial infarction cases of visitors to shahid madani specialized ...

Predicting the response at an unobserved location is a fundamental problem in spatial statistics. Given difficulty modeling dependence, especially nonstationary cases, model-based prediction intervals are risk of misspecification bias that can negatively affect their validity. Here we present new approach for model-free nonparametric based on conformal machinery. Our key observation data be tre...

This paper proposes the use of a language representation that specifies the relationship between terms of a sentence using question words. The proposed representation is tailored to help the search for documents containing an answer for a natural language question. This study presents the construction of this language model, the framework where it is used, and its evaluation.