Sentence examples for formulated the inference from inspiring English sources

To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions.

In order to overcome these two limitations, we re-formulate the inference problem using a RBPF.

We formulate the inference task as one of binary classification between small-molecule targets and non-targets (we assume non-targets have low enough affinity for the soluble competitor to have negligible effect on the SILAC ratio).

We formulate the inference problem as an optimization problem with a multi-objective function that measures: 1) The Goodness-of-Fit of a Boolean dynamic model with respect to the input data.

A subset of the methods based on a ODE description formulates the inference as a convex programming problem (Julius et al., 2009; Kulkarni et al., 2012; Zavlanos et al., 2011).

We formulate the inference of gene paths from a de Bruijn graph as a problem of aligning the graph against a set of reference genes, aiming to derive in the graph paths of sequence blocks (or contigs) that are most similar to the reference genes; each path represents a gene or a gene fragment that contains shorter gene fragments.

We first formulate the source inference problem in the IC model and prove its #P-completeness.

Recently, machine-learning theory has been used to formulate the network inference problem as a series of supervised gene selection procedures, where each gene in turn is designated as the target output.

The difficulty to separate direct from indirect regulations has been addressed with the formalism of Bayesian networks [ 16- 19], or by formulating the GRN inference problem as a feature selection problem [ 20].

To address the challenges concerning differential network inference using data-knowledge integrated approaches, we formulated the problem of learning the condition‐specific network structure and topological changes as a convex optimization problem.

Thus formulated, the argument is valid.