Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery

• URL: http://arxiv.org/abs/2012.12325v2
• Date: Thu, 11 Mar 2021 13:32:20 GMT
• Title: Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery
• Authors: Bin Liu, Konstantinos Pliakos, Celine Vens, Grigorios Tsoumakas
• Abstract summary: Drug-target interactions are predicted via structure-based drug similarities and sequence-based target protein similarities. Most existing similarity-based methods follow the transductive setting. A large amount of missing interactions in current DTI datasets hinders most DTI prediction methods. We propose a new method denoted as Weighted k Nearest Neighbor with Interaction Recovery (WkNNIR)
• Score: 5.8683934849211745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting drug-target interactions (DTI) via reliable computational methods is an effective and efficient way to mitigate the enormous costs and time of the drug discovery process. Structure-based drug similarities and sequence-based target protein similarities are the commonly used information for DTI prediction. Among numerous computational methods, neighborhood-based chemogenomic approaches that leverage drug and target similarities to perform predictions directly are simple but promising ones. However, most existing similarity-based methods follow the transductive setting. These methods cannot directly generalize to unseen data because they should be re-built to predict the interactions for new arriving drugs, targets, or drug-target pairs. Besides, many similarity-based methods, especially neighborhood-based ones, cannot handle directly all three types of interaction prediction. Furthermore, a large amount of missing interactions in current DTI datasets hinders most DTI prediction methods. To address these issues, we propose a new method denoted as Weighted k Nearest Neighbor with Interaction Recovery (WkNNIR). Not only can WkNNIR estimate interactions of any new drugs and/or new targets without any need of re-training, but it can also recover missing interactions. In addition, WkNNIR exploits local imbalance to promote the influence of more reliable similarities on the DTI prediction process. We also propose a series of ensemble methods that employ diverse sampling strategies and could be coupled with WkNNIR as well as any other DTI prediction method to improve performance. Experimental results over five benchmark datasets demonstrate the effectiveness of our approaches in predicting drug-target interactions. Lastly, we confirm the practical prediction ability of proposed methods to discover reliable interactions that not reported in the original benchmark datasets.

Related papers

• A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction [85.2792480737546]
  Existing methods fail to utilize global protein information during DTI prediction. Cross-field information fusion strategy is employed to acquire local and global protein information. Siamese drug-target interaction SiamDTI prediction method achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.
  arXiv  Detail & Related papers  (2024-05-23T13:25:20Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)
• Meta-Path-based Probabilistic Soft Logic for Drug-Target Interaction Prediction [36.08294497336554]
  Drug-target interaction (DTI) prediction aims at predicting whether a drug will be bounded to a target. Most of the recently proposed methods use single drug-drug similarity and target-target similarity information for DTI prediction. We propose a network-based drug-target interaction prediction approach.
  arXiv  Detail & Related papers  (2023-06-25T02:30:38Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• Fine-Grained Selective Similarity Integration for Drug-Target Interaction Prediction [10.1105462321649]
  We propose a Fine-Grained Selective similarity integration approach, called FGS. We evaluate FGS on five DTI prediction datasets under various prediction settings. Case studies on the analysis of similarity weights and on the verification of novel predictions confirm the practical ability of FGS.
  arXiv  Detail & Related papers  (2022-12-01T14:50:42Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Multi-View Substructure Learning for Drug-Drug Interaction Prediction [69.34322811160912]
  We propose a novel multi- view drug substructure network for DDI prediction (MSN-DDI) MSN-DDI learns chemical substructures from both the representations of the single drug (intra-view) and the drug pair (inter-view) simultaneously and utilizes the substructures to update the drug representation iteratively. Comprehensive evaluations demonstrate that MSN-DDI has almost solved DDI prediction for existing drugs by achieving a relatively improved accuracy of 19.32% and an over 99% accuracy under the transductive setting.
  arXiv  Detail & Related papers  (2022-03-28T05:44:29Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Optimizing Area Under the Curve Measures via Matrix Factorization for Drug-Target Interaction Prediction [7.385579678137434]
  This paper proposes two matrix factorization methods that optimize the area under the precision-recall curve (AUPR) and the receiver operating characteristic curve (AUC) Experimental results over four updated benchmark datasets show the superiority of the proposed methods in terms of the corresponding evaluation metric they optimize.
  arXiv  Detail & Related papers  (2021-05-01T14:48:32Z)
• DTI-SNNFRA: Drug-Target interaction prediction by shared nearest neighbors and fuzzy-rough approximation [0.0]
  We have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI) based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA) The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC.
  arXiv  Detail & Related papers  (2020-09-22T19:10:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.