ReviewBriefings in bioinformatics2022
Network approaches for modeling the effect of drugs and diseases.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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
18 citing papers in PubMed, 30 citations in OpenAlex.
- Network Model to Predict Age-Related Transcriptional Reprogramming.Aging cell · 2026Article
- Deciphering the Mechanisms of Statin-Ezetimibe Drug Combinations Using Boolean Logical Modeling and Transcriptomic Data.CPT: pharmacometrics & systems pharmacology · 2026Article
- Network biology to artificial intelligence: building the next generation of predictive drug discovery.Frontiers in immunology · 2026Review
- Drug-disease networks and drug repurposing.PLoS computational biology · 2025Article
- Targeting Irgm1 to combat osteoporosis: suppressing ROS and restoring bone remodeling.Cell death & disease · 2025Article
- Network Medicine as a Tool to Enhance Regenerative Rehabilitation Practice.American journal of physical medicine & rehabilitation · 2025Article
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Prioritizing FDA approved therapeutics for treating sepsis phenotypes: A network modeling approach based on neutrophil proteomics.Frontiers in immunology · 2025Article
- Network-based estimation of therapeutic efficacy and adverse reaction potential for prioritisation of anti-cancer drug combinations.Computational and structural biotechnology journal · 2025Article
- Untargeted Metabolome Atlas for Sleep Phenotypes in the Hispanic Community Health Study/Study of Latinos.medRxiv : the preprint server for health sciences · 2024Article
- Computational model for drug research.Briefings in bioinformatics · 2024Article
- Optimal fusion of genotype and drug embeddings in predicting cancer drug response.Briefings in bioinformatics · 2024Article
- Network-based cytokine inference implicates Oncostatin M as a driver of an inflammation phenotype in knee osteoarthritis.Aging cell · 2024Article
- Review
- A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.PLoS computational biology · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Assessing network-based methods in the context of system toxicology.Frontiers in pharmacology · 2023Article
- Adverse effect signature extraction and prediction for drugs treating COVID-19.Frontiers in genetics · 2022Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug's MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).
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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 Socratic graph.