ReviewBioMed research international2015
A survey on the computational approaches to identify drug targets in the postgenomic era.
Review in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 76 citations in OpenAlex.
- Sesamin Protects Against Polystyrene Microplastics-Induced Lung Injury via Attenuating Bcl2-Mediated Apoptosis.Antioxidants (Basel, Switzerland) · 2026Article
- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
- Systematic computational strategies for identifying protein targets and lead discovery.RSC medicinal chemistry · 2024Review
- The OREGANO knowledge graph for computational drug repurposing.Scientific data · 2023Article
- A Computational Workflow to Predict Biological Target Mutations: The Spike Glycoprotein Case Study.Molecules (Basel, Switzerland) · 2023Article
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
- Building a knowledge graph to enable precision medicine.Scientific data · 2023Article
- A Data-Driven Approach to Construct a Molecular Map ofVaccines · 2023Article
- Optimizing Genomic Control in Hit Network-Target Set Model Associations with Lung Adenocarcinoma.Journal of Cancer · 2023Article
- Article
- Precision drug design againstFrontiers in cellular and infection microbiology · 2023Article
- Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.Biology · 2022Article
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders.Pharmaceutics · 2022Review
- In silico Methods for Identification of Potential Therapeutic Targets.Interdisciplinary sciences, computational life sciences · 2022Review
- Discovery of Novel Drug Candidates for Alzheimer's Disease by Molecular Network Modeling.Frontiers in aging neuroscience · 2022Article
- A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach.Journal of clinical medicine · 2021Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization.PloS one · 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
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying drug targets plays essential roles in designing new drugs and combating diseases. Unfortunately, our current knowledge about drug targets is far from comprehensive. Screening drug targets in the lab is an expensive and time-consuming procedure. In the past decade, the accumulation of various types of omics data makes it possible to develop computational approaches to predict drug targets. In this paper, we make a survey on the recent progress being made on computational methodologies that have been developed to predict drug targets based on different kinds of omics data and drug property data.
Indexed as
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.