scholarly journals Predicting drug resistance in M. tuberculosis using a Long-term Recurrent Convolutional Networks architecture

2020 ◽  
Author(s):  
Amir Hosein Safari ◽  
Nafiseh Sedaghat ◽  
Alpha Forna ◽  
Hooman Zabeti ◽  
Leonid Chindelevitch ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Bacterial Isolate ◽  
State Of The Art ◽  
Model Parameters ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Machine Learning Methods ◽  
Input Feature ◽  
Per Gene

AbstractDrug resistance in Mycobacterium tuberculosis (MTB) may soon be a leading worldwide cause of death. One way to mitigate the risk of drug resistance is through methods that predict drug resistance in MTB using whole-genome sequencing (WGS) data. Existing machine learning methods for this task featurize the WGS data from a given bacterial isolate by defining one input feature per SNP. Here, we introduce a gene-centric method for predicting drug resistance in TB. We define one feature per gene according to the number of mutations in that gene in a give isolate. This representation greatly decreases the number of model parameters. We further propose a model that considers both gene order through a Long-term Recurrent Convolutional Network (LRCN) architecture, which combines convolutional and recurrent layers. We find that using these strategies yields a substantial, statistically-significant improvement over the state-of-the-art, and that this improvement is driven by the order of genes in the genome and their organization into operons.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

scholarly journals MR-GCN: Multi-Relational Graph Convolutional Networks based on Generalized Tensor Product

2020 ◽  
Author(s):  
Zhichao Huang ◽  
Xutao Li ◽  
Yunming Ye ◽  
Michael K. Ng
Keyword(s):  
Tensor Product ◽  
Convolution Operator ◽  
State Of The Art ◽  
Single Type ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Node Classification ◽  
Relational Graphs ◽  
Eigen Decomposition ◽  
Single Relation

Graph Convolutional Networks (GCNs) have been extensively studied in recent years. Most of existing GCN approaches are designed for the homogenous graphs with a single type of relation. However, heterogeneous graphs of multiple types of relations are also ubiquitous and there is a lack of methodologies to tackle such graphs. Some previous studies address the issue by performing conventional GCN on each single relation and then blending their results. However, as the convolutional kernels neglect the correlations across relations, the strategy is sub-optimal. In this paper, we propose the Multi-Relational Graph Convolutional Network (MR-GCN) framework by developing a novel convolution operator on multi-relational graphs. In particular, our multi-dimension convolution operator extends the graph spectral analysis into the eigen-decomposition of a Laplacian tensor. And the eigen-decomposition is formulated with a generalized tensor product, which can correspond to any unitary transform instead of limited merely to Fourier transform. We conduct comprehensive experiments on four real-world multi-relational graphs to solve the semi-supervised node classification task, and the results show the superiority of MR-GCN against the state-of-the-art competitors.

scholarly journals Topology Optimization based Graph Convolutional Network

2019 ◽  
Author(s):  
Liang Yang ◽  
Zesheng Kang ◽  
Xiaochun Cao ◽  
Di Jin ◽  
Bo Yang ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Network Topology ◽  
State Of The Art ◽  
Convolutional Network ◽  
Topological Information ◽  
Convolutional Networks ◽  
The Past ◽  
Attributed Network ◽  
Fully Connected ◽  
The Given

In the past few years, semi-supervised node classification in attributed network has been developed rapidly. Inspired by the success of deep learning, researchers adopt the convolutional neural network to develop the Graph Convolutional Networks (GCN), and they have achieved surprising classification accuracy by considering the topological information and employing the fully connected network (FCN). However, the given network topology may also induce a performance degradation if it is directly employed in classification, because it may possess high sparsity and certain noises. Besides, the lack of learnable filters in GCN also limits the performance. In this paper, we propose a novel Topology Optimization based Graph Convolutional Networks (TO-GCN) to fully utilize the potential information by jointly refining the network topology and learning the parameters of the FCN. According to our derivations, TO-GCN is more flexible than GCN, in which the filters are fixed and only the classifier can be updated during the learning process. Extensive experiments on real attributed networks demonstrate the superiority of the proposed TO-GCN against the state-of-the-art approaches.

scholarly journals DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1014
Author(s):  
Chengsheng Pan ◽  
Jiang Zhu ◽  
Zhixiang Kong ◽  
Huaifeng Shi ◽  
Wensheng Yang
Keyword(s):  
Feature Extraction ◽  
Network Traffic ◽  
Traffic Flows ◽  
Convolutional Network ◽  
Temporal Characteristics ◽  
Traffic Forecasting ◽  
Convolutional Networks ◽  
Forecasting Models ◽  
Dual Channel

Network traffic forecasting is essential for efficient network management and planning. Accurate long-term forecasting models are also essential for proactive control of upcoming congestion events. Due to the complex spatial-temporal dependencies between traffic flows, traditional time series forecasting models are often unable to fully extract the spatial-temporal characteristics between the traffic flows. To address this issue, we propose a novel dual-channel based graph convolutional network (DC-STGCN) model. The proposed model consists of two temporal components that characterize the daily and weekly correlation of the network traffic. Each of these two components contains a spatial-temporal characteristics extraction module consisting of a dual-channel graph convolutional network (DCGCN) and a gated recurrent unit (GRU). The DCGCN further consists of an adjacency feature extraction module (AGCN) and a correlation feature extraction module (PGCN) to capture the connectivity between nodes and the proximity correlation, respectively. The GRU further extracts the temporal characteristics of the traffic. The experimental results based on real network data sets show that the prediction accuracy of the DC-STGCN model overperforms the existing baseline and is capable of making long-term predictions.

scholarly journals Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes

2020 ◽  
Vol 34 (04) ◽  
pp. 5892-5899
Author(s):  
Ke Sun ◽  
Zhouchen Lin ◽  
Zhanxing Zhu
Keyword(s):  
Supervised Learning ◽  
State Of The Art ◽  
Difficult Problem ◽  
Superior Performance ◽  
Learning Approach ◽  
Training Algorithm ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Learning Tasks ◽  
Multi Stage

Graph Convolutional Networks (GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised (M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a popular form of self-supervised learning, and design corresponding aligning mechanism on the embedding space to refine the Multi-Stage Training Framework, resulting in M3S Training Algorithm. Finally, extensive experimental results verify the superior performance of our algorithm on graphs with few labeled nodes under different label rates compared with other state-of-the-art approaches.

scholarly journals Multi-Class Imbalanced Graph Convolutional Network Learning

2020 ◽  
Author(s):  
Min Shi ◽  
Yufei Tang ◽  
Xingquan Zhu ◽  
David Wilson ◽  
Jianxun Liu
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Convolutional Network ◽  
Network Learning ◽  
Convolutional Networks ◽  
Space Experiments ◽  
Latent Distribution ◽  
Adversarial Training ◽  
Quality Of Fit

Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization.

3D long-term recurrent convolutional networks for human sub-assembly recognition in human-robot collaboration

2020 ◽  
Vol 40 (4) ◽  
pp. 655-662 ◽  
Author(s):  
Xianhe Wen ◽  
Heping Chen
Keyword(s):  
High Precision ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Model Parameters ◽  
Assembly Process ◽  
Content Type ◽  
Convolutional Networks ◽  
3D Cnn ◽  
Human Robot Collaboration

Purpose Human assembly process recognition in human–robot collaboration (HRC) has been studied recently. However, most research works do not cover high-precision and long-timespan sub-assembly recognition. Hence this paper aims to deal with this problem. Design/methodology/approach To deal with the above-mentioned problem, the authors propose a 3D long-term recurrent convolutional networks (LRCN) by combining 3D convolutional neural networks (CNN) with long short-term memory (LSTM). 3D CNN behaves well in human action recognition. But when it comes to human sub-assembly recognition, the accuracy of 3D CNN is very low and the number of model parameters is huge, which limits its application in human sub-assembly recognition. Meanwhile, LSTM has the incomparable superiority of long-time memory and time dimensionality compression ability. Hence, by combining 3D CNN with LSTM, the new approach can greatly improve the recognition accuracy and reduce the number of model parameters. Findings Experiments were performed to validate the proposed method and preferable results have been obtained, where the recognition accuracy increases from 82% to 99%, recall ratio increases from 95% to 100% and the number of model parameters is reduced more than 8 times. Originality/value The authors focus on a new problem of high-precision and long-timespan sub-assembly recognition in the area of human assembly process recognition. Then, the 3D LRCN method is a new method with high-precision and long-timespan recognition ability for human sub-assembly recognition compared to 3D CNN method. It is extraordinarily valuable for the robot in HRC. It can help the robot understand what the sub-assembly human cooperator has done in HRC.

scholarly journals Shallow Graph Convolutional Network for Skeleton-Based Action Recognition

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 452
Author(s):  
Wenjie Yang ◽  
Jianlin Zhang ◽  
Jingju Cai ◽  
Zhiyong Xu
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Computational Cost ◽  
Receptive Fields ◽  
Recognition Task ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Spatial Graph ◽  
Graph Size ◽  
Skeleton Graph

Graph convolutional networks (GCNs) have brought considerable improvement to the skeleton-based action recognition task. Existing GCN-based methods usually use the fixed spatial graph size among all the layers. It severely affects the model’s abilities to exploit the global and semantic discriminative information due to the limits of receptive fields. Furthermore, the fixed graph size would cause many redundancies in the representation of actions, which is inefficient for the model. The redundancies could also hinder the model from focusing on beneficial features. To address those issues, we proposed a plug-and-play channel adaptive merging module (CAMM) specific for the human skeleton graph, which can merge the vertices from the same part of the skeleton graph adaptively and efficiently. The merge weights are different across the channels, so every channel has its flexibility to integrate the joints. Then, we build a novel shallow graph convolutional network (SGCN) based on the module, which achieves state-of-the-art performance with less computational cost. Experimental results on NTU-RGB+D and Kinetics-Skeleton illustrates the superiority of our methods.

scholarly journals MTDDI: a graph convolutional network framework for predicting Multi-Type Drug-Drug Interactions

2021 ◽  
Author(s):  
YueHua Feng ◽  
Shao-Wu Zhang ◽  
Qing-Qing Zhang ◽  
Chu-Han Zhang ◽  
Jian-Yu Shi
Keyword(s):  
Deep Learning ◽  
Drug Interactions ◽  
Adverse Reactions ◽  
Underlying Mechanism ◽  
Structural Relationship ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Machine Learning Methods ◽  
Single Fold ◽  
Predictive Methods

Abstract Although the polypharmacy has both higher therapeutic efficacy and less drug resistance in combating complex diseases, drug-drug interactions (DDIs) may trigger unexpected pharmacological effects, such as side effects, adverse reactions, or even serious toxicity. Thus, it is crucial to identify DDIs and explore its underlying mechanism (e.g., DDIs types) for polypharmacy safety. However, the detection of DDIs in assays is still time-consuming and costly, due to the need of experimental search over a large drug combinational space. Machine learning methods have been proved as a promising and efficient method for preliminary DDI screening. Most shallow learning-based predictive methods focus on whether a drug interacts with another or not. Although deep learning (DL)-based predictive methods address a more realistic screening task for identifying the DDI types, they only predict the DDI types of known DDI, ignoring the structural relationship between DDI entries, and they also cannot reveal the knowledge about the dependence between DDI types. Thus, here we proposed a novel end-to-end deep learning-based predictive method (called MTDDI) to predict DDIs as well as its types, exploring the underlying mechanism of DDIs. MTDDI designs an encoder derived from enhanced deep relational graph convolutional networks to capture the structural relationship between multi-type DDI entries, and adopts the tensor-like decoder to uniformly model both single-fold interactions and multi-fold interactions to reflect the relation between DDI types. The results show that our MTDDI is superior to other state-of-the-art deep learning-based methods. For predicting the multi-type DDIs with unknown DDIs in case of both single-fold DDIs and multi-fold DDIs, we validated the effectiveness and the practical capability of our MTDDI. More importantly, MTDDI can reveal the dependency between DDI types. These crucial observations are beneficial to uncover the mechanism and regularity of DDIs.

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