Predicting Drug-Target Interaction Using Deep Matrix Factorization

Manoochehri, Hafez Eslami; Nourani, Mehrdad

doi:10.1109/biocas.2018.8584817

Cited by 21 publications

(12 citation statements)

References 28 publications

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“…Noting that traditional MF methods are unable to detect nonlinear properties, a deep MF (DMF) method is proposed in [68]. The DMF approach first constructs negative samples using a K-Nearest Neighbor (kNN) method and then builds an interaction matrix.…”

Section: Related Workmentioning

confidence: 99%

Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction

Agyemang,

Wu,

Kpiebaareh

et al. 2020

Preprint

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The drug discovery stage is a vital part of the drug development process and forms part of the initial stages of the development pipeline. In recent times, machine learning-based methods are actively being used to model drug-target interactions for rational drug discovery due to the successful application of these methods in other domains. In machine learning approaches, the numerical representation of molecules is vital to the performance of the model. While significant progress has been made in molecular representation engineering, this has resulted in several descriptors for both targets and compounds. Also, the interpretability of model predictions is a vital feature that could have several pharmacological applications. In this study, we propose a self-attention-based, multi-view representation learning approach for modeling drug-target interactions. We evaluated our approach using three large-scale kinase datasets and compared six variants of our method to 16 baselines. Our experimental results demonstrate the ability of our method to achieve high accuracy and offer biologically plausible interpretations using neural attention.

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Section: Related Workmentioning

confidence: 99%

Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction

Agyemang,

Wu,

Kpiebaareh

et al. 2020

Preprint

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“…In 2018, Manoochehri and Nourani [24] used Deep Matrix Factorization (DMF) to predict drug-target interaction. They discussed two approaches.…”

Section: Literature Surveymentioning

confidence: 99%

Predicting Drug Interaction With Adenosine Receptors Using Machine Learning and SMOTE Techniques

2019

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Cancer is one of the most influential factors causing death in the world. Adenosine which is a molecule, found in all human cells by coupling with G protein it turns into an adenosine receptor. Adenosine receptor is an important target for cancer therapy. Adenosine stops the growth of malignant tumor cells such as lymphoma, melanoma and prostate carcinoma. Adenosine is activated by interacting with drugs to stop tumor cells from spreading and cure cancer disease. This research aims to predict drugs and potential drug candidates that interact with adenosine receptors. We built a machine learning model using three different classification techniques: Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) then we chose the best technique after comparing the results. Unlike other researches, we used the drug side effect integrated into drug fingerprint as a feature to train our model to classify drugs (interacting and non-interacting) with adenosine receptors. We ranked the interacting drugs with adenosine receptors based on drug side effects to find the most preferred drug (least side effect) among several drugs, which helps in drug design. Most existing datasets contain drugs, targets and the interactions between them, neglecting drug side effects. We formed a new dataset that has the drug side effect. The new dataset is composed of 400 drugs, 794 targets and 3990 drug side effects. Since the dataset was imbalanced we applied Synthetic Minority Oversampling Technique (SMOTE). After conducting experiments, RF achieved the best classification performance with an accuracy of 75.09%.

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“…The deep matrix factorization is used for recommender systems and has been shown to be superior to traditional matrix factorization 12 . This strategy has recently been used in the prediction of Drug-Target interactions 13 . Taken together, most present studies are focused on only one type of biological network and need specific biochemical features or cannot handle big and sparse data of all types of heterogeneous multi-layer networks.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach to predict inter-omics interactions in multi-layer networks

Borhani

Ghaisari

Abedi

et al. 2021

Preprint

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Despite enormous achievements in production of high throughput datasets, constructing comprehensive maps of interactions remains a major challenge. The lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease. Here, Data Integration with Deep Learning (DIDL), a novel nonlinear deep learning method is proposed to predict inter-omics interactions. It consists of an encoder that automatically extracts features for biomolecules according to existing interactions, and a decoder that predicts novel interactions. The applicability of DIDL is assessed with different networks namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, the validity of novel predictions is assessed by literature surveys. Furthermore, DIDL outperformed state-of-the-art methods. Area under the curve, and area under the precision-recall curve for all three networks were more than 0.85 and 0.83, respectively. DIDL has several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to sparsity. In addition, tensor decomposition structure, predictions solely based on existing interactions and biochemical data independence makes DIDL applicable for a variety of biological networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.

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Predicting Drug-Target Interaction Using Deep Matrix Factorization

Cited by 21 publications

References 28 publications

Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction

Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction

Predicting Drug Interaction With Adenosine Receptors Using Machine Learning and SMOTE Techniques

A deep learning approach to predict inter-omics interactions in multi-layer networks

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