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We explored the possible biological roles of the 56 genes by performing network analysis using the commercial tool MetaCore from GeneGo Inc. which builds gene networks based on molecular interactions acquired from experiments-based literature reports [ 11].
We have used these modified nanotubes as AFM tips to titrate the acid and base groups, to image patterned samples based on molecular interactions, and to measure the binding force between single protein ligand pairs.
This study is focused on the realization of nanodevices for nano and molecular electronics, based on molecular interactions in a metal-molecule-metal (M-M-M) structure.
The pursued project is aiming at the integration of existing data resources of small molecules that are combined with tools for the prediction of biological effects based on molecular interactions.
Biological function is based on molecular interactions, and these are a consequence of macromolecular structures.
Network analysis based on molecular interactions suggests a potential role of these 56 genes in tissue differentiation and carcinogenesis.
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For this purpose, two different ways of representing the binding site were compared: Sequence based fingerprints and descriptors based on Molecular Interaction Fields (MIFs).
In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data.
Owing to the completion of human genome sequencing projects and the advancement of molecular medicine, extensive system biology efforts have been made to discover new combinations based on molecular interaction networks [ 14, 15] in the past few years [ 16- 19].
Another advantage of our model is its ability to provide an estimate of the propensity of assemblies with different levels of symmetry breaking based on the kinetic equations derived from a statistical formalism based on molecular interaction [ 32, 69- 71].
Motivation: Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database.
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based on molecular dynamics
based on electrochemical interactions
based on molecular principles
based on molecular genetics
based on molecular targets
based on molecular analyses
based on possible interactions
based on molecular mechanics
based on molecular patterns
based on molecular descriptors
based on prior interactions
based on complex interactions
based on molecular techniques
based on human interactions
based on molecular data
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