4.7 Article

A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Biochemical Research Methods

Using drug descriptions and molecular structures for drug-drug interaction extraction from literature

Masaki Asada et al.

Summary: This study introduces a new method for extracting drug-drug interactions (DDIs) from literature, utilizing external drug database information and large-scale plain text. The results demonstrate that integrating large-scale raw text information can greatly improve DDI extraction performance, while simultaneously using drug description and molecular structure information can significantly enhance performance across all DDI types. The effective combination of plain text, drug description, and molecular structure information is essential for improving DDI extraction.

BIOINFORMATICS (2021)

Article Biochemical Research Methods

MUFFIN: multi-scale feature fusion for drug-drug interaction prediction

Yujie Chen et al.

Summary: The study highlights the importance of predicting DDIs in medicine, leading to the development of the MUFFIN deep learning model that combines drug molecular structure and semantic information from knowledge graphs for improved accuracy.

BIOINFORMATICS (2021)

Article Biochemical Research Methods

SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization

Yue Yu et al.

Summary: The paper introduces a new method called SumGNN, which utilizes a knowledge summarization graph neural network to improve multi-typed DDI pharmacological effect prediction using large biomedical knowledge graphs. Results show significant performance gains, particularly in low data relation types. Additionally, SumGNN provides interpretable predictions through generated reasoning paths for each prediction.

BIOINFORMATICS (2021)

Article Biochemical Research Methods

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction

Arnold K. Nyamabo et al.

Summary: Adverse drug-drug interactions (DDIs) can pose serious risks to the organism due to interference between different drugs' mechanisms of action. Existing computational methods for identifying DDIs have room for improvement and may benefit from a new deep learning framework called SSI-DDI, which focuses on pairwise interactions between substructures for improved prediction performance.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Biochemical Research Methods

A multimodal deep learning framework for predicting drug-drug interaction events

Yifan Deng et al.

BIOINFORMATICS (2020)

Article Mathematical & Computational Biology

ISCMF: Integrated similarity-constrained matrix factorization for drug-drug interaction prediction

Narjes Rohani et al.

NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS (2020)

Review Biochemical Research Methods

Graph convolutional networks for computational drug development and discovery

Mengying Sun et al.

BRIEFINGS IN BIOINFORMATICS (2020)

Article Pharmacology & Pharmacy

Pharmacodynamic Drug-Drug Interactions

Jin Niu et al.

CLINICAL PHARMACOLOGY & THERAPEUTICS (2019)

Article Multidisciplinary Sciences

Network-based prediction of drug combinations

Feixiong Cheng et al.

NATURE COMMUNICATIONS (2019)

Article Biochemical Research Methods

Novel deep learning model for more accurate prediction of drug-drug interaction effects

Geonhee Lee et al.

BMC BIOINFORMATICS (2019)

Article Biochemistry & Molecular Biology

DrugBank 5.0: a major update to the DrugBank database for 2018

David S. Wishart et al.

NUCLEIC ACIDS RESEARCH (2018)

Article Biochemical Research Methods

Modeling polypharmacy side effects with graph convolutional networks

Marinka Zitnik et al.

BIOINFORMATICS (2018)

Article Mathematical & Computational Biology

Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization

Hui Yu et al.

BMC SYSTEMS BIOLOGY (2018)

Review Biochemistry & Molecular Biology

Identifying Drug-Target Interactions with Decision Templates

Xiao-Ying Yan et al.

CURRENT PROTEIN & PEPTIDE SCIENCE (2018)

Article Multidisciplinary Sciences

Deep learning improves prediction of drug-drug and drug-food interactions

Jae Yong Ryu et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)

Article Biochemical Research Methods

A probabilistic approach for collective similarity-based drug-drug interaction prediction

Dhanya Sridhar et al.

BIOINFORMATICS (2016)

Article Multidisciplinary Sciences

Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects

Ping Zhang et al.

SCIENTIFIC REPORTS (2015)

Article Computer Science, Information Systems

Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties

Feixiong Cheng et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2014)

Article Biochemistry & Molecular Biology

INDI: a computational framework for inferring drug interactions and their associated recommendations

Assaf Gottlieb et al.

MOLECULAR SYSTEMS BIOLOGY (2012)

Article Chemistry, Medicinal

Extended-Connectivity Fingerprints

David Rogers et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Review Pharmacology & Pharmacy

Towards a mechanism-based analysis of pharmacodynamic drug-drug interactions in vivo

DM Jonker et al.

PHARMACOLOGY & THERAPEUTICS (2005)