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This information is collected by the many interaction databases in existence today (1, 2).
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Many molecular interaction databases have chosen the PSI-MI format for providing data to their users.
The incorrect or absent identification of the source organism of a participant molecule was recently reported as a common error in many external interaction databases [ 65].
Many gene annotation, pathway, and protein interaction databases are primarily compiled for human genes/proteins.
L2-PPI scores are imputed in edges whose proteins do not actually interact according to PPI databases, yet who share many interaction partners.
Most of the large interaction databases include data for many different species.
The A-C (Drug-Disease) interactions inferred using B (gene) terms are evaluated by examining how many inferred interactions are matched in well-known interaction databases such as PharmGKB and CTD.
APID2NET retrieves all the possible information on protein-protein interaction from five interaction databases namely, Database of Interacting Proteins [44], Biomolecular Interaction Network Database [45], IntAct [46], Molecular Interactions Database [47] and Human Protein Reference Database [48].
In the evaluation, we focused on how many interactions were matched with two answer sets (known interaction databases - PharmGKB and CTD) as the performance evaluation.
Many of these databases focus on one particular type of relational interaction, ranging from protein-protein interaction databases [ 1], gene-chemical databases [ 4], and disease-gene databases [ 2], and are often constructed using data mining methods complemented by manual curation.
As PPI interaction databases and genomic sequences become more available, this approach will provide innovative drug targets for many different parasites and pathogens.
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