Graph Convolution Networks Using Message Passing and Multi-Source Similarity Features for Predicting circRNA-Disease Association

Mudiyanselage, Thosini Bamunu; Lei, Xiujuan; Senanayake, Nipuna; Zhang, Yanqing; Pan, Yi

doi:10.1109/bibm49941.2020.9313455

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…De novo drug design (Hartenfeller and Schneider, 2010) through existing computer technology can speed up drug development and save research costs. The tasks involved in de novo drug design include molecular generation (Gómez-Bombarelli et al, 2016;Cao and Kipf, 2018;Jin et al, 2018;You et al, 2018;Madhawa et al, 2019;Popova et al, 2019;Zhang et al, 2019;Hong et al, 2020;Zang and Wang, 2020;Bagal et al, 2021), drug and drug interactions (DDI) (Li et al, 2021;Lin et al, 2021;Lyu et al, 2021;Zhao et al, 2021), disease associations (Ding et al, 2020;Lei and Zhang, 2020;Mudiyanselage et al, 2020;Lei X.-J. et al, 2021;Lei X. et al, 2021;Wang Y. et al, 2021;Lei and Zhang, 2021;Yang and Lei, 2021;Zhang et al, 2021), and so on.…”

Section: Introductionmentioning

confidence: 99%

Cross-Adversarial Learning for Molecular Generation in Drug Design

Cui

et al. 2022

Front. Pharmacol.

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Molecular generation is an important but challenging task in drug design, as it requires optimization of chemical compound structures as well as many complex properties. Most of the existing methods use deep learning models to generate molecular representations. However, these methods are faced with the problems of generation validity and semantic information of labels. Considering these challenges, we propose a cross-adversarial learning method for molecular generation, CRAG for short, which integrates both the facticity of VAE-based methods and the diversity of GAN-based methods to further exploit the complex properties of Molecules. To be specific, an adversarially regularized encoder-decoder is used to transform molecules from simplified molecular input linear entry specification (SMILES) into discrete variables. Then, the discrete variables are trained to predict property and generate adversarial samples through projected gradient descent with corresponding labels. Our CRAG is trained using an adversarial pattern. Extensive experiments on two widely used benchmarks have demonstrated the effectiveness of our proposed method on a wide spectrum of metrics. We also utilize a novel metric named Novel/Sample to measure the overall generation effectiveness of models. Therefore, CRAG is promising for AI-based molecular design in various chemical applications.

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Section: Introductionmentioning

confidence: 99%

Cross-Adversarial Learning for Molecular Generation in Drug Design

Cui

et al. 2022

Front. Pharmacol.

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“…Although miRNA expression profiles and drug substructures play important roles in the association prediction, more features about miRNAs and drugs should be taken into account to improve the performances. In recent years, the graph learning methods, especially graph neural networks (GNN), showed great success in biomedical association prediction ( Mudiyanselage et al, 2020 ; Yu et al, 2020 ; Fu et al, 2021 ; Lei et al, 2021 ; Liu et al, 2021 ; Yang and Lei, 2021 ; Zhang et al, 2021a ). Thus, it is necessary to develop GNN-based multimodal method to address above mentioned issues and improve the miRNA-drug resistance association prediction.…”

Section: Introductionmentioning

confidence: 99%

MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network

et al. 2022

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MiRNAs can regulate genes encoding specific proteins which are related to the efficacy of drugs, and predicting miRNA-drug resistance associations is of great importance. In this work, we propose an attentive multimodal graph convolution network method (AMMGC) to predict miRNA-drug resistance associations. AMMGC learns the latent representations of drugs and miRNAs from four graph convolution sub-networks with distinctive combinations of features. Then, an attention neural network is employed to obtain attentive representations of drugs and miRNAs, and miRNA-drug resistance associations are predicted by the inner product of learned attentive representations. The computational experiments show that AMMGC outperforms other state-of-the-art methods and baseline methods, achieving the AUPR score of 0.2399 and the AUC score of 0.9467. The analysis demonstrates that leveraging multiple features of drugs and miRNAs can make a contribution to the miRNA-drug resistance association prediction. The usefulness of AMMGC is further validated by case studies.

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“…Deep learning operations such as convolution which takes simple correlations between pixels within Euclidean data has contributed to the better performance in these data. But graphs are extensively employed data structures which capture interactions between different data instances and have the potential for better learning [13][14][15][16]. As a result, there are recent efforts to extend deep learning operations to graph data which have irregularities such as variable sized and unordered nodes.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks

Mudiyanselage,

Senanayake,

et al. 2021

Preprint

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The novel corona virus has introduced significant challenges due to its rapid spreading nature through respiratory transmission. As a result, there is a huge demand for Artificial Intelligence (AI) based quick disease diagnosis methods as an alternative to high demand tests such as Polymerase Chain Reaction (PCR).Chest X-ray (CXR) Image analysis is such cost-effective radiography technique due to resource availability and quick screening. But, a sufficient and systematic data collection that is required by complex deep leaning (DL) models is more difficult and hence there are recent efforts that utilize transfer learning to address this issue. Still these transfer learnt models suffer from lack of generalization and increased bias to the training dataset resulting poor performance for unseen data. Limited correlation of the transferred features from the pre-trained model to a specific medical imaging domain like X-ray and overfitting on fewer data can be reasons for this circumstance. In this work, we propose a novel Graph Convolution Neural Network (GCN) that is capable of identifying bio-markers of Covid-19 pneumonia from CXR images and meta information about patients. The proposed method exploits important relational knowledge between data instances and their features using graph representation and applies convolution to learn the graph data which is not possible with conventional convolution on Euclidean domain. The results of extensive experiments of proposed model on binary (Covid vs normal) and three class (Covid, normal, other pneumonia) classification problems outperform different benchmark transfer learnt models, hence overcoming the aforementioned drawbacks.

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Graph Convolution Networks Using Message Passing and Multi-Source Similarity Features for Predicting circRNA-Disease Association

Cited by 7 publications

References 35 publications

Cross-Adversarial Learning for Molecular Generation in Drug Design

Cross-Adversarial Learning for Molecular Generation in Drug Design

MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network

Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks

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