Improving cancer driver gene identification using multi-task learning on graph convolutional network

Peng, Wei; Tang, Qihe; Dai, Wei; Chen, Tielin

doi:10.1093/bib/bbab432

Cited by 63 publications

(36 citation statements)

References 35 publications

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“…To measure quality of the predicted functions by each method, we use two assessment criteria: AUROC (area under the receiver-operating curve) [ 37 ] and average F-score [ 38 , 39 ]. AUROC is widely used in performance evaluation for protein function prediction.…”

Section: Resultsmentioning

confidence: 99%

A tensor-based bi-random walks model for protein function prediction

Xiong

Jiang

et al. 2022

BMC Bioinformatics

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Background The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein–protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. Results In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. Conclusions The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations.

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Section: Resultsmentioning

confidence: 99%

A tensor-based bi-random walks model for protein function prediction

Xiong

Jiang

et al. 2022

BMC Bioinformatics

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“…In this work, the performance of PHN is evaluated by two cross validations, including leave-one-out cross validation and ten-fold cross validation. To measure the quality of predicted functions by our method, we adopt the ROC (Receiver-Operating Curve) [ 27 , 28 ] as an assessment. The ROC curve is plotted with FPR (False Positive Rates) and TPR (True Positive Rates) [ 29 ], which is widely used in performance evaluation for protein function prediction.…”

Section: Resultsmentioning

confidence: 99%

Protein function annotation based on heterogeneous biological networks

Luo²,

Xiong³

et al. 2022

BMC Bioinformatics

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Background Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data. Results In this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively. Conclusions We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

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“…In this article, FBN defines the nodes as brain regions, and the edge between these regions is determined by the relationship between their blood-oxygen-level dependent (BOLD) time series recorded by fMRI. In recent years, with the rise of deep graph convolutional networks, state-of-the-art performance has been achieved in their applications in different fields, such as social networks (Dowlagar and Mamidi, 2021 ; Liu et al, 2022 ), computer vision (Han et al, 2021 ; Zou and Tang, 2021 ), and gene prediction (Yu et al, 2021 ; Peng et al, 2022 ). Meanwhile, deep graph convolutional networks have also achieved satisfactory success in disease prediction tasks (Tang et al, 2021 ; Yu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification

Feng

Huang

Le³

et al. 2022

Front. Aging Neurosci.

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Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%.

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Improving cancer driver gene identification using multi-task learning on graph convolutional network

Cited by 63 publications

References 35 publications

A tensor-based bi-random walks model for protein function prediction

A tensor-based bi-random walks model for protein function prediction

Protein function annotation based on heterogeneous biological networks

A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification

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