Pages that link to "Q27902360"

The following pages link to DASPfind: new efficient method to predict drug–target interactions (Q27902360):

Displaying 43 items.

• DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning (Q27902260) (← links)
• Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem (Q28817549) (← links)
• PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction (Q36322043) (← links)
• DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction. (Q42334070) (← links)
• DDR: Efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. (Q48633458) (← links)
• Integrative cancer pharmacogenomics to establish drug mechanism of action: drug repurposing. (Q48634710) (← links)
• HIDEEP: a systems approach to predict hormone impacts on drug efficacy based on effect paths (Q47144109) (← links)
• iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting. (Q47155719) (← links)
• Web-based drug repurposing tools: a survey (Q47801769) (← links)
• Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory. (Q47867878) (← links)
• Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. (Q49604743) (← links)
• DPubChem: a web tool for QSAR modeling and high-throughput virtual screening. (Q55381330) (← links)
• Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (Q55963652) (← links)
• Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome (Q56992819) (← links)
• Network-Based Methods for Prediction of Drug-Target Interactions (Q57816959) (← links)
• Survey of Similarity-based Prediction of Drug-protein Interactions (Q58079602) (← links)
• PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations (Q58584856) (← links)
• BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network (Q59136386) (← links)
• Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure (Q62492324) (← links)
• A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL) (Q63353622) (← links)
• Computational Prediction of Drug-Target Interactions via Ensemble Learning (Q64119813) (← links)
• Biomedical data and computational models for drug repositioning: a comprehensive review (Q89644704) (← links)
• A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder (Q89665960) (← links)
• FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction (Q90182654) (← links)
• Inferring Latent Disease-lncRNA Associations by Faster Matrix Completion on a Heterogeneous Network (Q90401699) (← links)
• CFSBoost: Cumulative feature subspace boosting for drug-target interaction prediction (Q90714463) (← links)
• A path-based computational model for long non-coding RNA-protein interaction prediction (Q90874470) (← links)
• Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk (Q91261517) (← links)
• Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting (Q91307555) (← links)
• Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks (Q91358986) (← links)
• Efficient Gaussian sample specific network marker discovery and drug enrichment analysis validation (Q91375763) (← links)
• Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities (Q91871988) (← links)
• DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features (Q92304586) (← links)
• Machine learning approaches and databases for prediction of drug-target interaction: a survey paper (Q92724567) (← links)
• A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches (Q93163692) (← links)
• Drug-target interaction prediction using semi-bipartite graph model and deep learning (Q97418218) (← links)
• Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network (Q97639868) (← links)
• DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning (Q112608445) (← links)
• DTiGEMS : drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques (Q112620691) (← links)
• Knowledge graphs and their applications in drug discovery (Q112807694) (← links)
• DTIP-TC2A: An analytical framework for drug-target interactions prediction methods (Q113318394) (← links)
• Predicting Drug-target Interactions via FM-DNN Learning (Q128300853) (← links)
• Predicting the binding affinities of compound–protein interactions by random forest using network topology features (Q129416437) (← links)