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In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene disease associations.
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For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature.
The photographic evidence featured in the inquiry.
When possible, we highlight evidence for features of human neocortical architecture and function that cannot be easily explained as correlates of brain size and, hence, might be more directly associated with the evolution of uniquely human cognitive capacities.
On the other hand, there is also evidence for features that characterize non-pathogenic organisms, the so-called "antivirulence" genes.
The growing evidence for features of social capital as determinants of health and wellbeing has simultaneously highlighted its potential as a pathway to other determinants, and therefore its relevance to a range of policy sectors including welfare, education, families and communities, employment, housing, urban development and planning and justice.
In conclusion, we find that the DCETTO is a feasible method for developing values for larger descriptive systems such as EQ-5D-5L, and find evidence supporting important design features for future valuation studies that use the DCETTO.
Evidence theory contains powerful features for uncertainty analysis and can be effectively employed to address the epistemic uncertainty, which is attributed to a lack of information in complex engineering problems.
Ensembl reported no evidence (genscan models, ESTs, UniGene features) for 52% of these sequences while significant evidence was available only for 12%.
The system may help not only to identify relevant circumstantial evidences among various biological features for assessing reliability of PPIs, but also to understand the characteristics of true interacting protein pairs based on the alternating decision tree.
While these lncRNA genes exhibit low sequence conservation, we provide evidence that the secondary structural features for many lncRNAs have been conserved.
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CEO of Professional Science Editing for Scientists @ prosciediting.com