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In systems developmental biology where characterization of complexity of various time course data likely leads to address inference of temporal dynamic patterns from transcriptome, we are not often really interested in exactly how only a single gene is temporally differentially expressed at a particular time point or period.
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This paper addresses inference of a broad class of latent time-series observed via nonlinear functions.
We address some derivations for model inference of BGS-NMF parameters and hyperparameters.
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.
The problem addressed in this work is the inference of gene-gene interactions that are specifically associated with a phenotype (such as a particular cancer) from two sets of gene expression data, one in the presence and one in the absence of the phenotype, and without use of prior biological knowledge.
Other issues to address: Reviewer #1: The section "Model-based inference of the auditory transduction function" was very difficult to follow.
Such concerns may be addressed by using Bayesian approaches to the inference of adaptive evolution, [ 20, 21] which should account for parameter uncertainty.
These questions were recently addressed using surrogate methods, those that do not require inference of phylogenetic trees, such as the analysis of BLAST scores.
Our method directly addresses the challenges imposed by overlapping and fuzzy nucleosomes, their detection and the inference of their characteristics.
A most recent approach whose theoretical framework supports inference of all dense, but not necessarily connected subgraphs in interaction networks (without particularly addressing gene expression) is [9].
Pythoscape addresses these issues and provides a software framework to create PSNs and develop new analyses for inference of functional properties in proteins.
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CEO of Professional Science Editing for Scientists @ prosciediting.com