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Our strategy for the identification of miRNA-mRNA regulatory modules integrated two independent, yet complementary types of information: inverse expression relationships and computational target predictions.
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Barring this information, these inverse problem approaches can lead to erroneous localizations.
Borrowing strength, i.e. the optimal estimation of experiment-specific unknown forward model inputs, is introduced as a means for combining information in inverse problems.
Briefly, the methods account for all of the longitudinal data and adjustments are made for individuals who have missing information using inverse probability weighting.
Our computational approach integrates two independent, but complementary types of information: the inverse expression relationships inferred from the paired expression profiles of miRNAs and mRNAs, and the computational target prediction based on sequence information.
where (hat {mathbf {J}}^{jj}) is the (j,j)- entry of the estimated inverse information matrix (hat {mathbf {J}}^{-1}).
Finally, using a geological model as prior information in geostatistical inverse models results in the preservation of geological features, especially in areas where drawdown data are not available.
The purpose of this work is to study the incorporation of quantitative imaging information in radiotherapy inverse optimization through total energy minimization (Energy hereafter).
Here we study the calculation of the Fisher information matrix, the inverse of the Cramer Rao lower bound, from a system theoretic point of view.
The transmission delay is defined as the latency needed for transmitting the same amount of information, which is inverse to the transmission rate.
Regularization introduces additional information in an inverse problem in order to solve an ill-posed problem or to prevent over-fitting.
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