DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism

Article Biochemical Research Methods

A dual graph neural network for drug-drug interactions prediction based on molecular structure and interactions

Mei Ma et al.

Summary: This study develops a new model for predicting drug-drug interactions, improving the prediction performance and identifying potential treatment combinations for COVID-19.

PLOS COMPUTATIONAL BIOLOGY (2023)

Add to Collection

Article Mathematical & Computational Biology

MSResG: Using GAE and Residual GCN to Predict Drug–Drug Interactions Based on Multi-source Drug Features

Lin Guo et al.

Interdisciplinary Sciences-Computational Life Sciences (2023)

Add to Collection

Article Biochemical Research Methods

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

Yue-Hua Feng et al.

Summary: Current machine learning-based methods have made impressive predictions in mono-type and multi-type drug-drug interactions (DDIs) scenarios, but they fail to consider the enhancing and depressive pharmacological changes triggered by DDIs. Additionally, these pharmacological changes are asymmetric due to the different roles of the two drugs involved in an interaction. To address these issues, this study utilizes Balance and Status theories from social networks to reveal the topological patterns in directed pharmacological DDIs. A novel graph representation learning model named SGRL-DDI is then proposed to achieve multitask prediction of DDIs by integrating relation graph convolutional networks with Balance and Status patterns. Experimental results using DDI entries collected from DrugBank demonstrate the superiority of the SGRL-DDI model compared to other state-of-the-art methods. Moreover, the practical effectiveness of the model is demonstrated through a version-dependent test.

BRIEFINGS IN BIOINFORMATICS (2023)

Add to Collection

Review Biochemical Research Methods

Representation of molecules for drug response prediction

Xin An et al.

Summary: This article focuses on the application of machine learning in drug response prediction, and specifically discusses the implementation and application examples of molecular representation methods.

BRIEFINGS IN BIOINFORMATICS (2022)

Add to Collection

Article Biochemical Research Methods

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism

Shenggeng Lin et al.

Summary: One of the main problems with the joint use of multiple drugs is the potential for adverse drug interactions and side effects. This study proposes a novel method, MDF-SA-DDI, which predicts drug-drug interaction events using multi-source drug fusion, multi-source feature fusion, and transformer self-attention mechanism.

BRIEFINGS IN BIOINFORMATICS (2022)

Add to Collection

Article Biochemical Research Methods

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

Jinxian Wang et al.

Summary: In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The model, called DeepDDS, achieved better performance than other methods in predicting drug synergy. Additionally, we explored the interpretability of the model and found important chemical substructures of drugs. DeepDDS is considered an effective tool for prioritizing synergistic drug combinations for further experimental validation.

BRIEFINGS IN BIOINFORMATICS (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Integration of an imbalance framework with novel high-generalizable classifiers for radiomics-based distant metastases prediction of advanced nasopharyngeal carcinoma

Yuanpeng Zhang et al.

Summary: In this study, a high-generalizable classifier MERGE and an imbalance framework SUS are developed to address model overfitting and data imbalance in radiomics studies. The integration of class compactness graph into multi-kernel regression helps weaken the overfitting problem, while the sensitivity-based under-sampling imbalance ensemble framework SUS utilizes potentially informative data effectively. Experimental results demonstrate promising performance compared with benchmarking methods.

KNOWLEDGE-BASED SYSTEMS (2022)

Add to Collection

Article Mathematical & Computational Biology

DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier

Yan Zhang et al.

Summary: Accurate prediction of drug-target interactions is a key challenge in drug science, and the proposed method DeepStack-DTIs achieves higher accuracy compared to existing methods by extracting various features and utilizing a stacked ensemble classifier. The method shows excellent predictive ability on different datasets, providing new insights for drug-target interaction prediction.

INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

RPI-MDLStack: Predicting RNA-protein interactions through deep learning with stacking strategy and LASSO

Bin Yu et al.

Summary: In this study, a deep learning-based framework called RPI-MDLStack is developed for predicting RNA-protein interactions (RPI). By optimizing feature extraction and using a stacking ensemble strategy, RPI-MDLStack achieves high prediction accuracy.

APPLIED SOFT COMPUTING (2022)

Add to Collection

Article Engineering, Biomedical

Identification of DNA modification sites based on elastic net and bidirectional gated recurrent unit with convolutional neural network

Bin Yu et al.

Summary: In this study, a prediction method named 4mCi6mA-BGC is developed to predict DNA N4-methylcytosine (4mC) sites and DNA N6-methyladenine (6mA) sites. Using a deep learning framework, this method can identify DNA modification sites and shows better prediction performance compared to existing methods.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Add to Collection

Article Biochemical Research Methods

Directed graph attention networks for predicting asymmetric drug-drug interactions

Yi-Yang Feng et al.

Summary: This paper presents a new method (DGAT-DDI) for predicting asymmetric drug-drug interactions. By learning the embeddings of different roles and the chemical structure of drugs, and combining the number of interaction partners and the similarity between embeddings, this method shows superiority in prediction tasks and is validated in practical applications.

BRIEFINGS IN BIOINFORMATICS (2022)

Add to Collection

Article Biochemical Research Methods

3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction

Haohuai He et al.

Summary: This study proposes a new deep learning architecture that enhances the prediction ability of drug-drug interactions (DDIs) by utilizing 3D molecular graph structure and position information. The experimental results demonstrate that the proposed model outperforms other baseline models in DDI prediction and provides interpretability through weight visualization.

BRIEFINGS IN BIOINFORMATICS (2022)

Add to Collection

Article Biochemical Research Methods

Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions

Xiaorui Su et al.

Summary: This paper proposes a KG-based drug-drug interaction (DDI) prediction framework called DDKG, which utilizes KG information and attention mechanism. Experimental results show that DDKG outperforms existing algorithms on different evaluation metrics.

BRIEFINGS IN BIOINFORMATICS (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Malsite-Deep: Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy

Minghui Wang et al.

Summary: In this paper, a new prediction model, Malsite-Deep, is proposed for predicting protein malonylation sites. The model combines feature extraction and deep neural networks to achieve accurate predictions, and its performance is evaluated on multiple test sets.

KNOWLEDGE-BASED SYSTEMS (2022)

Add to Collection

Review Biochemical Research Methods

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection

Yang Qiu et al.

Summary: This paper provides a comprehensive review of computational methods for detecting drug-drug interactions (DDIs). It discusses three categories of methods: literature-based extraction methods, machine learning-based prediction methods, and pharmacovigilance-based data mining methods. The paper presents the research background, data sources, representative approaches, and evaluation metrics for each category. It also discusses the current challenges and potential opportunities for future directions in DDI detection.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2022)

Add to Collection

Article Biochemical Research Methods

LaGAT: link-aware graph attention network for drug-drug interaction prediction

Yue Hong et al.

Summary: In this study, a Link-aware Graph Attention method called LaGAT is proposed for DDI prediction. LaGAT can generate different attention pathways for drug entities based on different drug pair links, leading to better capture of semantic relationships and achieving superior performance in prediction.

BIOINFORMATICS (2022)

Add to Collection

Article

Improved prediction of drug-drug interactions using ensemble deep neural networks

Thanh Hoa Vo et al.

Medicine in Drug Discovery (2022)

Add to Collection

Article Biochemical Research Methods

Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches

Betul Guvenc Paltun et al.

Summary: Predicting the response of cancer cell lines to specific drugs is a central problem in personalized medicine. Choosing informative data sources and methods that can efficiently incorporate multiple sources is a challenging part of successful analysis in personalized medicine.

BRIEFINGS IN BIOINFORMATICS (2021)

Add to Collection

Review Biochemical Research Methods

A survey and systematic assessment of computational methods for drug response prediction

Jinyu Chen et al.

Summary: This study systematically evaluates 17 representative methods for drug response prediction developed in the past 5 years on four large public datasets with nine metrics, providing insights and lessons for future research into drug response prediction.

BRIEFINGS IN BIOINFORMATICS (2021)

Add to Collection

Article Automation & Control Systems

MpsLDA-ProSVM: Predicting multi-label protein subcellular localization by wMLDAe dimensionality reduction and ProSVM classifier

Qi Zhang et al.

Summary: The paper introduces a new prediction model, MpsLDA-ProSVM, which accurately predicts the specific subcellular localization of multi-label proteins in cells. By utilizing various coding algorithms and a weighted multi-label linear discriminant analysis framework, the model demonstrates high accuracy in virus, plant, Gram-positive bacteria, and Gram-negative bacteria datasets.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Qingmei Zhang et al.

Summary: In this paper, a method named StackPDB is proposed for predicting DNA-binding proteins (DBPs) using a stacked ensemble classifier, which shows excellent predictive ability in the context of high-cost and low-efficiency experimental methods.

APPLIED SOFT COMPUTING (2021)

Add to Collection

Article Biochemical Research Methods

Accurate prediction of multi-label protein subcellular localization through multi-view feature learning with RBRL classifier

Qi Zhang et al.

Summary: This article introduces a prediction method called Mps-mvRBRL for multi-label protein subcellular localization, achieving high prediction accuracy for different types of bacteria through feature fusion and weighted linear discriminant analysis.

BRIEFINGS IN BIOINFORMATICS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Multi independent latent component extension of naive Bayes classifier

Sasan H. Alizadeh et al.

Summary: Naive Bayes classifier is known for its simplicity and performance, but many generalizations compromise simplicity and lead to more complex models. The Multi Independent Latent Component Naive Bayes Classifier uses latent variables to maintain the model structure while reducing complexities in classification and learning. Experimental results show that it outperforms state-of-the-art classifiers in terms of AUC and ACC.

KNOWLEDGE-BASED SYSTEMS (2021)

Add to Collection

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)

Add to Collection

Article Computer Science, Artificial Intelligence

TP-DDI: Transformer-based pipeline for the extraction of Drug-Drug Interactions

Dimitrios Zaikis et al.

Summary: Drug-Drug Interaction (DDI) extraction involves identifying drug entities and potential interactions between drug pairs in biomedical literature. Machine Learning approaches are essential for reducing the laborious task of drug discovery, but there is still room for improvement in terms of prediction accuracy.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Add to Collection

Article Biology

DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network

Cheng Chen et al.

Summary: The study introduces a novel method DNN-DTIs for predicting drug-target interactions, demonstrating superior accuracy compared to other predictors, especially suitable for drug repositioning research.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Add to Collection

Article Biochemical Research Methods

SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting

Bin Yu et al.

BIOINFORMATICS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Gaussian prior based adaptive synthetic sampling with non-linear sample space for imbalanced learning

Tianlun Zhang et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Add to Collection

Article Mathematics, Applied

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network

Alex Sherstinsky

PHYSICA D-NONLINEAR PHENOMENA (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Squeeze-and-Excitation Networks

Jie Hu et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Systematic ensemble model selection approach for educational data mining

MohammadNoor Injadat et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Add to Collection

Article Biochemical Research Methods

DPDDI: a deep predictor for drug-drug interactions

Yue-Hua Feng et al.

BMC BIOINFORMATICS (2020)

Add to Collection

Article Genetics & Heredity

GTB-PPI: Predict Protein-protein Interactions Based on L1-regularized Logistic Regression and Gradient Tree Boosting

Bin Yu et al.

GENOMICS PROTEOMICS & BIOINFORMATICS (2020)

Add to Collection

Article Biochemical Research Methods

Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net

Yaning Liu et al.

ANALYTICAL BIOCHEMISTRY (2020)

Add to Collection

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)

Add to Collection

Article Biochemical Research Methods

Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

Xiaoying Wang et al.

BIOINFORMATICS (2019)

Add to Collection

Article Biotechnology & Applied Microbiology

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure

Han Shi et al.

GENOMICS (2019)

Add to Collection

Article Biochemical Research Methods

Modeling polypharmacy side effects with graph convolutional networks

Marinka Zitnik et al.

BIOINFORMATICS (2018)

Add to Collection

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)

Add to Collection

Article Biochemical Research Methods

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

Wen Zhang et al.

BMC BIOINFORMATICS (2017)

Add to Collection

Article Engineering, Biomedical

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples

He-Hua Zhang et al.

BIOMEDICAL ENGINEERING ONLINE (2016)

Add to Collection

Article Computer Science, Artificial Intelligence

ENN: Extended Nearest Neighbor Method for Pattern Recognition

Bo Tang et al.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2015)

Add to Collection

Article Public, Environmental & Occupational Health

The Weber Effect and the United States Food and Drug Administration's Adverse Event Reporting System (FAERS): Analysis of Sixty-Two Drugs Approved from 2006 to 2010

Keith B. Hoffman et al.

DRUG SAFETY (2014)

Add to Collection

Article Biochemical Research Methods

Similarity-based modeling in large-scale prediction of drug-drug interactions

Santiago Vilar et al.

NATURE PROTOCOLS (2014)

Add to Collection

Article Biochemistry & Molecular Biology

DrugBank 4.0: shedding new light on drug metabolism

Vivian Law et al.

NUCLEIC ACIDS RESEARCH (2014)

Add to Collection

Article Computer Science, Artificial Intelligence

Random projection ensemble learning with multiple empirical kernels

Zhe Wang et al.

KNOWLEDGE-BASED SYSTEMS (2013)

Add to Collection

Article Computer Science, Artificial Intelligence

Feature selection via maximizing global information gain for text classification

Changxing Shang et al.

KNOWLEDGE-BASED SYSTEMS (2013)

Add to Collection

Article Cell Biology

Data-Driven Prediction of Drug Effects and Interactions

Nicholas P. Tatonetti et al.

SCIENCE TRANSLATIONAL MEDICINE (2012)

Add to Collection

Article Biochemistry & Molecular Biology

Lessons for fragment library design: analysis of output from multiple screening campaigns

I-Jen Chen et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2009)

Add to Collection

Article Computer Science, Artificial Intelligence

Extremely randomized trees

P Geurts et al.

MACHINE LEARNING (2006)

Add to Collection

Article Biochemistry & Molecular Biology

DrugBank: a comprehensive resource for in silico drug discovery and exploration

David S. Wishart et al.

NUCLEIC ACIDS RESEARCH (2006)

Add to Collection

Article Statistics & Probability

Model selection and estimation in regression with grouped variables

M Yuan et al.

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2006)

Add to Collection

Review Pharmacology & Pharmacy

Novel technologies for virtual screening

T Lengauer et al.

DRUG DISCOVERY TODAY (2004)

Add to Collection

DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism

Related references

Export Citation

Share Paper