scholarly journals Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Dejun Jiang ◽  
Zhenxing Wu ◽  
Chang-Yu Hsieh ◽  
Guangyong Chen ◽  
Ben Liao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Computational Cost ◽  
Large Dataset ◽  
Predictive Capacity ◽  
Classification Tasks ◽  
Graph Neural Networks ◽  
Public Datasets

AbstractGraph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.

scholarly journals Could Graph Neural Networks Learn Better Molecular Representation for Drug Discovery? A Comparison Study of Descriptor-based and Graph-based Models

2020 ◽  
Author(s):  
Dejun Jiang ◽  
Zhenxing Wu ◽  
Chang-Yu Hsieh ◽  
Guangyong Chen ◽  
Ben Liao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Computational Cost ◽  
Large Dataset ◽  
Predictive Capacity ◽  
Classification Tasks ◽  
Graph Neural Networks ◽  
Public Datasets

Abstract Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.

scholarly journals Could Graph Neural Networks Learn Better Molecular Representation for Drug Discovery? A Comparison Study of Descriptor-based and Graph-based Models

2020 ◽  
Author(s):  
Dejun Jiang ◽  
Zhenxing Wu ◽  
Chang-Yu Hsieh ◽  
Guangyong Chen ◽  
Ben Liao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Computational Cost ◽  
Large Dataset ◽  
Predictive Capacity ◽  
Classification Tasks ◽  
Graph Neural Networks ◽  
Public Datasets

Abstract Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.

scholarly journals Estimating latent positions of actors using Neural Networks in R with GCN4R

2020 ◽  
Author(s):  
Joshua Levy ◽  
Carly Bobak ◽  
Brock Christensen ◽  
Louis Vaickus ◽  
James O’Malley
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Learning Community ◽  
Biological Networks ◽  
Peer Effects ◽  
Prediction Models ◽  
Research Community ◽  
Social Network Data ◽  
Covariate Information ◽  
Graph Neural Networks

AbstractNetwork analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While numerous methods have been developed for the R statistical software to analyze such data, deep learning methods have not been implemented in this language. Here, we introduce GCN4R, an R library for fitting graph neural networks on independent networks to aggregate actor covariate information to yield meaningful embeddings for a variety of network-based tasks (e.g. community detection, peer effects models, social influence). We provide an extensive overview of insights and methods utilized by the deep learning community on learning on social and biological networks, followed by a tutorial that demonstrates some of the capabilities of the GCN4R framework to make these methods more accessible to the R research community.

scholarly journals High Performance Gesture Recognition via Effective and Efficient Temporal Modeling

2019 ◽  
Author(s):  
Yang Yi ◽  
Feng Ni ◽  
Yuexin Ma ◽  
Xinge Zhu ◽  
Yuankai Qi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
High Performance ◽  
Short Term Memory ◽  
State Of The Art ◽  
Computational Cost ◽  
Temporal Modeling ◽  
Spatiotemporal Features ◽  
Public Datasets

State-of-the-art hand gesture recognition methods have investigated the spatiotemporal features based on 3D convolutional neural networks (3DCNNs) or convolutional long short-term memory (ConvLSTM). However, they often suffer from the inefficiency due to the high computational complexity of their network structures. In this paper, we focus instead on the 1D convolutional neural networks and propose a simple and efficient architectural unit, Multi-Kernel Temporal Block (MKTB), that models the multi-scale temporal responses by explicitly applying different temporal kernels. Then, we present a Global Refinement Block (GRB), which is an attention module for shaping the global temporal features based on the cross-channel similarity. By incorporating the MKTB and GRB, our architecture can effectively explore the spatiotemporal features within tolerable computational cost. Extensive experiments conducted on public datasets demonstrate that our proposed model achieves the state-of-the-art with higher efficiency. Moreover, the proposed MKTB and GRB are plug-and-play modules and the experiments on other tasks, like video understanding and video-based person re-identification, also display their good performance in efficiency and capability of generalization.

scholarly journals A public unified bug dataset for java and its assessment regarding metrics and bug prediction

2020 ◽  
Vol 28 (4) ◽  
pp. 1447-1506 ◽  
Author(s):  
Rudolf Ferenc ◽  
Zoltán Tóth ◽  
Gergely Ladányi ◽  
István Siket ◽  
Tibor Gyimóthy
Keyword(s):  
Prediction Models ◽  
Source Code ◽  
Decision Tree Algorithm ◽  
Large Dataset ◽  
Code Analysis ◽  
Project Learning ◽  
Code Metrics ◽  
Public Datasets ◽  
Source Code Metrics ◽  
Cross Project

AbstractBug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metric-based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction. We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction. We found that there are statistically significant differences in the values of the original and the newly calculated metrics; furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.

scholarly journals A weighted patient network-based framework for predicting chronic diseases using graph neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haohui Lu ◽  
Shahadat Uddin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cardiovascular Disease ◽  
Chronic Disease ◽  
Pulmonary Disease ◽  
Chronic Diseases ◽  
Prediction Models ◽  
Disease Prediction ◽  
Chronic Pulmonary Disease ◽  
Graph Neural Networks

AbstractChronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.

scholarly journals Graph Neural Architecture Search

2020 ◽  
Author(s):  
Yang Gao ◽  
Hong Yang ◽  
Peng Zhang ◽  
Chuan Zhou ◽  
Yue Hu
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Domain Knowledge ◽  
Search Space ◽  
Recurrent Network ◽  
Validation Data ◽  
Data Set ◽  
Neural Architecture ◽  
Real World Datasets ◽  
Graph Neural Networks

Graph neural networks (GNNs) emerged recently as a powerful tool for analyzing non-Euclidean data such as social network data. Despite their success, the design of graph neural networks requires heavy manual work and domain knowledge. In this paper, we present a graph neural architecture search method (GraphNAS) that enables automatic design of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and trains the recurrent network with policy gradient to maximize the expected accuracy of the generated architectures on a validation data set. Furthermore, to improve the search efficiency of GraphNAS on big networks, GraphNAS restricts the search space from an entire architecture space to a sequential concatenation of the best search results built on each single architecture layer. Experiments on real-world datasets demonstrate that GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of validation set accuracy. Moreover, in a transfer learning task we observe that graph neural architectures designed by GraphNAS, when transferred to new datasets, still gain improvement in terms of prediction accuracy.

scholarly journals The Use of Stochastic Models for Short-Term Prediction of Water Parameters of the Thesaurus Dam, River Nestos, Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 634 ◽  
Author(s):  
Antonis Sentas ◽  
Lina Karamoutsou ◽  
Nikos Charizopoulos ◽  
Thomas Psilovikos ◽  
Aris Psilovikos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stochastic Models ◽  
Prediction Models ◽  
Short Term ◽  
Predictive Capacity ◽  
Water Parameters ◽  
Term Prediction ◽  
Artificial Neural ◽  
Short Term Prediction

The scope of this paper is to evaluate the short-term predictive capacity of the stochastic models ARIMA, Transfer Function (TF) and Artificial Neural Networks for water parameters, specifically for 1, 2 and 3 steps forward (m = 1, 2 and 3). The comparison of statistical parameters indicated that ARIMA models could be proposed as short-term prediction models. In some cases that TF models resulted in better predictions, the difference with ARIMA was minimal and since the latter are simpler in their construction, they are proposed for short-term prediction. Artificial Neural Networks didn’t show a good short-term predictive capacity in comparison with the aforementioned models.

Learning from Substitutable and Complementary Relations for Graph-based Sequential Product Recommendation

2022 ◽  
Vol 40 (2) ◽  
pp. 1-28
Author(s):  
Wei Zhang ◽  
Zeyuan Chen ◽  
Hongyuan Zha ◽  
Jianyong Wang
Keyword(s):  
Neural Networks ◽  
Data Driven ◽  
Seamless Integration ◽  
Specific Product ◽  
Product Recommendation ◽  
Target User ◽  
Complementary Graph ◽  
Sequential Product ◽  
Graph Neural Networks ◽  
Public Datasets

Sequential product recommendation, aiming at predicting the products that a target user will interact with soon, has become a hotspot topic. Most of the sequential recommendation models focus on learning from users’ interacted product sequences in a purely data-driven manner. However, they largely overlook the knowledgeable substitutable and complementary relations between products. To address this issue, we propose a novel Substitutable and Complementary Graph-based Sequential Product Recommendation model, namely, SCG-SPRe. The innovations of SCG-SPRe lie in its two main modules: (1) The module of interactive graph neural networks jointly encodes the high-order product correlations in the substitutable graph and the complementary graph into two types of relation-specific product representations. (2) The module of kernel-enhanced transformer networks adaptively fuses multiple temporal kernels to characterize the unique temporal patterns between a candidate product to be recommended and any interacted product in a target behavior sequence. Thanks to the seamless integration of the two modules, SCG-SPRe obtains candidate-dependent user representations for different candidate products to compute the corresponding ranking scores. We conduct extensive experiments on three public datasets, demonstrating SCG-SPRe is superior to competitive sequential recommendation baselines and validating the benefits of explicitly modeling the product-product relations.

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