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From these analyses, we derived a network that describes the influence of miRNAs and their regulation in human OC.
To obtain a network that describes the parasitic metabolism as completely as possible, data from multiple online resources have been integrated in the compilation process.
Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations.
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The kernel of the system is a probabilistic network that describes the presentation characteristics of cancer of the oesophagus and the pathophysiological processes of invasion and metastasis.
Subsequently, the canonical pathway was augmented with compound→target interactions obtained from PubChem (Supplementary Table S5) and with hypothetical phosphoprotein→cytokine release interactions to result in an integrated network that describes both levels (intracellular and extracellular) of signal transduction.
However, when searching for the most parsimonious network that describes a collection of characters, it becomes necessary to add additional cost considerations to prefer simpler structures, such as trees over networks.
Here, we conducted the first systems-level analysis of miRNA evolution in a human transcription factor (TF -miRNA regulaTF -miRNAoregulatoryscribes the regulatory relationetworkg that miRNAs, andescribesgenes.
To test our hypothesis that the regulatory network that describes leukaemic propagation in B-ALL is different to the stem cell programme in a hierarchical system, we performed a PCA as before but this time using Eppert's 42-gene (48-probe) signature derived from candidate human AML stem cell populations capable of repopulating immunodeficient mice (Eppert et al, 2011).
The structure of the population can be usefully represented by means of a graph or network that describes the interaction connectivity.
We use a mixed-integer nonlinear programming (MINLP) approach to select the best network that describes an existing MCHE.
We used the seed-network that describes photoreceptor differentiation (Fig. 8) to address this question.
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