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DTI-CDF: a CDF model towards the prediction of DTIs based on hybrid features

https://doi.org/10.1101/657973
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Prediction of protein cellular attributes using pseudo‐amino acid composition
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Discrimination of Intracellular and Extracellular Proteins Using Amino Acid Composition and Residue-pair Frequencies
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Protein secondary structure prediction based on position-specific scoring matrices 1 1Edited by G. Von Heijne
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Chemogenomic Approaches to Infer Drug–Target Interaction Networks
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Predicting drug–target interaction using positive-unlabeled learning
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Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure
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Boosting compound-protein interaction prediction by deep learning
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Drug-target interaction prediction using ensemble learning and dimensionality reduction
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Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response
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Deep-Learning-Based Drug–Target Interaction Prediction
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Extended-Connectivity Fingerprints
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Predicting Drug–Target Interactions Using Probabilistic Matrix Factorization
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Relating protein pharmacology by ligand chemistry
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Similarity network fusion for aggregating data types on a genomic scale
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A comprehensive map of molecular drug targets
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How many drug targets are there?
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How were new medicines discovered?
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iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting
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Predicting drug-target interactions by dual-network integrated logistic matrix factorization
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Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions
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Drug–target interaction prediction by random walk on the heterogeneous network
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Similarity-based machine learning methods for predicting drug–target interactions: a brief review
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Drug–target interaction prediction: databases, web servers and computational models
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Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes
inferredcites →https://doi.org/10.1093/bioinformatics/btn162openalex-snapshot
Prediction of drug–target interaction networks from the integration of chemical and genomic spaces
inferredcites →https://doi.org/10.1093/bioinformatics/btp433openalex-snapshot
Supervised prediction of drug–target interactions using bipartite local models
inferredcites →https://doi.org/10.1093/bioinformatics/btr500openalex-snapshot
Gaussian interaction profile kernels for predicting drug–target interaction
inferredcites →https://doi.org/10.1093/bioinformatics/bts360openalex-snapshot
Predicting drug–target interactions from chemical and genomic kernels using Bayesian matrix factorization
inferredcites →https://doi.org/10.1093/bioinformatics/bts413openalex-snapshot
Drug target prediction using adverse event report systems: a pharmacogenomic approach
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Drug–target interaction prediction by learning from local information and neighbors
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Predicting drug-target interactions using restricted Boltzmann machines
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Deep mining heterogeneous networks of biomedical linked data to predict novel drug–target associations
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DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
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BRENDA, the enzyme database: updates and major new developments
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SuperTarget and Matador: resources for exploring drug-target relationships
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KEGG for linking genomes to life and the environment
inferredcites →https://doi.org/10.1093/nar/gkm958openalex-snapshot
DrugBank: a knowledgebase for drugs, drug actions and drug targets
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The SIDER database of drugs and side effects
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SRP: A concise non-parametric similarity-rank-based model for predicting drug-target interactions
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Prediction drug-target interaction networks based on semi-supervised learning method
inferredcites →https://doi.org/10.1109/icdar.1995.598994openalex-snapshot
Random decision forests
inferredcites →https://doi.org/10.1109/ijcnn.2016.7727339openalex-snapshot
Large-scale prediction of drug-target interactions from deep representations
inferredcites →https://doi.org/10.1109/jbhi.2015.2513200openalex-snapshot
Predicting Drug–Target Interactions With Multi-Information Fusion
inferredcites →https://doi.org/10.1109/tcbb.2016.2530062openalex-snapshot
Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization
inferredcites →https://doi.org/10.1142/9789812799623_0053openalex-snapshot
THE SPECTRUM KERNEL: A STRING KERNEL FOR SVM PROTEIN CLASSIFICATION
inferredcites →https://doi.org/10.1145/1143844.1143874openalex-snapshot
The relationship between Precision-Recall and ROC curves
inferredcites →https://doi.org/10.1145/2487575.2487670openalex-snapshot
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
inferredcites →https://doi.org/10.1186/1752-0509-4-s2-s6openalex-snapshot
Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces
inferredcites →https://doi.org/10.1186/1758-2946-3-33openalex-snapshot
Open Babel: An open chemical toolbox
inferredcites →https://doi.org/10.1186/s12859-016-0890-3openalex-snapshot
A multiple kernel learning algorithm for drug-target interaction prediction
inferredcites →https://doi.org/10.1186/s12859-016-1005-xopenalex-snapshot
Predicting drug target interactions using meta-path-based semantic network analysis
inferredcites →https://doi.org/10.1186/s12859-017-1845-zopenalex-snapshot
VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization
inferredcites →https://doi.org/10.1186/s12859-018-2095-4openalex-snapshot
BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data
inferredcites →https://doi.org/10.1371/journal.pcbi.1002503openalex-snapshot
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
inferredcites →https://doi.org/10.1371/journal.pcbi.1004760openalex-snapshot
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction
inferredcites →https://doi.org/10.1371/journal.pone.0062975openalex-snapshot
A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks
inferredcites →https://doi.org/10.1371/journal.pone.0080129openalex-snapshot
Predicting Drug-Target Interactions Using Drug-Drug Interactions
inferredcites →https://doi.org/10.2174/1389203718666161108091609openalex-snapshot
Drug-Target Interactions: Prediction Methods and Applications
inferredcites →https://doi.org/10.2307/3802723openalex-snapshot
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
inferredcites →https://doi.org/10.24963/ijcai.2017/497openalex-snapshot
Deep Forest: Towards An Alternative to Deep Neural Networks
inferredcites →https://doi.org/10.24963/ijcai.2018/483openalex-snapshot
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
inferredcites →https://doi.org/10.3390/molecules23092208openalex-snapshot
Machine Learning for Drug-Target Interaction Prediction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-25568-6_6openalex-snapshot
Advances in Deep Learning for Drug–Target Interaction Prediction: A Systematic and Comprehensive Review
inferred← citeshttps://doi.org/10.1007/s11704-020-0180-0openalex-snapshot
DeepM6ASeq-EL: prediction of human N6-methyladenosine (m6A) sites with LSTM and ensemble learning
inferred← citeshttps://doi.org/10.3389/fmicb.2020.580382openalex-snapshot
T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm
inferred← citeshttps://doi.org/10.3390/ijms21103585openalex-snapshot
Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope
inferred← citeshttps://doi.org/10.3390/molecules27165131openalex-snapshot
Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding
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