Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

DAPredict: a database for drug action phenotype prediction.

Database : the journal of biological databases and curation - 18 Jan 2024

Meng Qingkang, Cai Yiyang, Zhou Kun, Xu Fei, Huo Diwei, Xie Hongbo, Yu Meini, Zhang Denan, Chen Xiujie

Abstract excerpt

The phenotypes of drug action, including therapeutic actions and adverse drug reactions (ADRs), are important indicators for evaluating the druggability of new drugs and repositioning the approved drugs. Here, we provide a user-friendly database, DAPredict (http://bio-bigdata.hrbmu.edu.cn/DAPredict), in which our novel original drug action phenotypes prediction algorithm (Yang,J., Zhang,D., Liu,L. et al. (2021)...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Databases, Factual

Share to Drug-Related Side Effects and Adverse Reactions

Share to Drug Repositioning

Share to Genomics

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 38242684
DOI: 10.1093/database/baad095
PMCID: PMC10799211

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

DAPredict: a database for drug action phenotype prediction.PMID 38242684Read ↗

Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations.Semantic proximity Read ↗

Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data.Semantic proximity Read ↗

D4: Deep Drug-drug interaction Discovery and DemystificationSemantic proximity Read ↗

Literature-based prediction of novel drug indications considering relationships between entities.Semantic proximity Read ↗

Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data.Semantic proximity Read ↗

Mining drug-disease relationships as a complement to medical genetics-based drug repositioning: Where a recommendation system meets genome-wide association studies.Semantic proximity Read ↗

Mapping drug biology to disease genetics to discover drug impacts on the human phenomeSemantic proximity Read ↗

Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenariosSemantic proximity Read ↗

Disentangling the contribution of disease genes to drug therapeutic and side effectsSemantic proximity Read ↗

DIGEP‐Pred 2.0: A web application for predicting drug‐induced cell signaling and gene expression changesSemantic proximity Read ↗

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based AnalysisSemantic proximity Read ↗

The DEER database: a bridge connecting drugs, environmental effects, and regulations.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.