ArticleScientific reports2018
An integrative approach to develop computational pipeline for drug-target interaction network analysis.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.Journal of cheminformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the general principles governing the functioning of biological networks is a major challenge of the current era. Functionality of biological networks can be observed from drug and target interaction perspective. All possible modes of operations of biological networks are confined by the interaction analysis. Several of the existing approaches in this direction, however, are data-driven and thus lack potential to be generalized and extrapolated to different species. In this paper, we demonstrate a systems pharmacology pipeline and discuss how the network theory, along with gene ontology (GO) analysis, co-expression analysis, module re-construction, pathway mapping and structure level analysis can be used to decipher important properties of biological networks with the aim to propose lead molecule for the therapeutic interventions of various diseases.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.