MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms

• URL: http://arxiv.org/abs/2501.07884v1
• Date: Tue, 14 Jan 2025 06:50:56 GMT
• Title: MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms
• Authors: XinXin Ge, Yi-Ting Lee, Shan-Ju Yeh,
• Abstract summary: Drug combination therapies have shown promising therapeutic efficacy in complex diseases and have demonstrated the potential to reduce drug resistance. The huge number of possible drug combinations makes it difficult to screen them all in traditional experiments. In this study, we proposed MD-Syn, a computational framework, which is based on the multidimensional feature fusion method and multi-head attention mechanisms.
• Score: 0.0
• License:
• Abstract: Drug combination therapies have shown promising therapeutic efficacy in complex diseases and have demonstrated the potential to reduce drug resistance. However, the huge number of possible drug combinations makes it difficult to screen them all in traditional experiments. In this study, we proposed MD-Syn, a computational framework, which is based on the multidimensional feature fusion method and multi-head attention mechanisms. Given drug pair-cell line triplets, MD-Syn considers one-dimensional and two-dimensional feature spaces simultaneously. It consists of a one-dimensional feature embedding module (1D-FEM), a two-dimensional feature embedding module (2D-FEM), and a deep neural network-based classifier for synergistic drug combination prediction. MD-Syn achieved the AUROC of 0.919 in 5-fold cross-validation, outperforming the state-of-the-art methods. Further, MD-Syn showed comparable results over two independent datasets. In addition, the multi-head attention mechanisms not only learn embeddings from different feature aspects but also focus on essential interactive feature elements, improving the interpretability of MD-Syn. In summary, MD-Syn is an interpretable framework to prioritize synergistic drug combination pairs with chemicals and cancer cell line gene expression profiles. To facilitate broader community access to this model, we have developed a web portal (https://labyeh104-2.life.nthu.edu.tw/) that enables customized predictions of drug combination synergy effects based on user-specified compounds.

Related papers

• KITE-DDI: A Knowledge graph Integrated Transformer Model for accurately predicting Drug-Drug Interaction Events from Drug SMILES and Biomedical Knowledge Graph [0.11049608786515838]
  Drug-Drug Interactions (DDI) can cause significant bodily injury and even death. Most contemporary research for predicting DDI events relies on either information from Biomedical Knowledge graphs (KG) or drug SMILES. In this study, we propose a KG-integrated Transformer architecture to generate an end-to-end fully automated Machine Learning pipeline.
  arXiv  Detail & Related papers  (2024-12-08T00:49:57Z)
• MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation [64.4838301776267]
  Multi-channel Interaction Network (MIN) is a novel framework designed to predict drug-target interaction (DTI) MIN incorporates a representation learning module and a multi-channel interaction module. MIN is not only a potent tool for DTI prediction but also offers fresh insights into the prediction of protein binding sites.
  arXiv  Detail & Related papers  (2024-11-23T05:38:36Z)
• Joint Multimodal Transformer for Emotion Recognition in the Wild [49.735299182004404]
  Multimodal emotion recognition (MMER) systems typically outperform unimodal systems. This paper proposes an MMER method that relies on a joint multimodal transformer (JMT) for fusion with key-based cross-attention.
  arXiv  Detail & Related papers  (2024-03-15T17:23:38Z)
• SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design [64.69434941796904]
  We propose a novel setting and models for in-context drug synergy learning. We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context.
  arXiv  Detail & Related papers  (2023-06-19T17:03:46Z)
• 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)
• Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization [16.41618129467975]
  We take a multi-layered network perspective, where different layers correspond to different similarity metrics between drugs and targets. To fully take advantage of topology information captured in multiple views, we develop an optimization framework, called MDMF, for DTI prediction. The framework learns vector representations of drugs and targets that not only retain higher-order proximity across all hyper-layers, but also approximates the interactions with their inner product.
  arXiv  Detail & Related papers  (2022-01-24T08:02:05Z)
• MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy [2.446672595462589]
  We propose a multimodal graph neural network that can predict the synergistic effect of drug combinations for cancer treatment. Our model captures the representation based on the context of drugs at multiple scales based on a drug-protein interaction network and metadata. We demonstrate that the model makes high-quality predictions over a wide range of cancer cell line tissues.
  arXiv  Detail & Related papers  (2021-10-28T13:10:25Z)
• DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations [0.9854322576538699]
  computational screening has become an important way to prioritize drug combinations. DeepDDS was superior to competitive methods by more than 16% predictive precision.
  arXiv  Detail & Related papers  (2021-07-06T08:25:43Z)
• AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions [0.9176056742068811]
  Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures.
  arXiv  Detail & Related papers  (2020-12-24T13:33:07Z)
• Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas [65.64363834322333]
  Confidence Guided SAMR (CG-SAMR) synthesizes data from lesion information to multi-modal anatomic sequences. module guides the synthesis based on confidence measure about the intermediate results. experiments on real clinical data demonstrate that the proposed model can perform better than the state-of-theart synthesis methods.
  arXiv  Detail & Related papers  (2020-08-06T20:20:22Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

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.