This project applies the Expectation-Maximization (EM) algorithm to estimate the relative abundances of RNA isoforms based on RNA-seq read data. The task involved modeling how sequencing reads map to ...

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Obtaining the optimal solution for the aforementioned objective function becomes increasingly challenging as the dimensionality of the data exceeds three. The core principle of the ...

The least absolute shrinkage and selection operator (Lasso) estimation of regression coefficients can be expressed as Bayesian posterior mode estimation of the regression coefficients under various ...

Abstract: Expectation-Maximization (EM) is typically used to compute maximum likelihood estimates given incomplete samples and estimated the parameters. We proposed a new algorithm for generating an ...

Abstract: In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However, most of these methods presume that the measurement matrix is completely known ...