Deep Learning Approach for Breast Cancer InClust 5 Prediction based on Multiomics Data Integration

Alkhateeb, Abedalrhman; Zhou, Li; Tabl, Ashraf Abou; Rueda, Luis

doi:10.1145/3388440.3415992

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Many previous studies depend on early data concatenation 21 or independent analysis of the late merging of the omics data in the prediction model. 14 Data embedding techniques try to extract the meaningful relationships using visual maps, then merge those relationships in the CNN model to find the global associations from the spatial representation of the omics. The model utilizes UMAP, which tries to find the global and local structure of the relationships among the features and represent it on a two-dimensional map.…”

Section: Discussionmentioning

confidence: 99%

“…Then, all 3 omics data were normalized on an average scale, and genes that are not listed in HUGO format were eliminated. The last step was to substantially distinguish the mutated genes through the MutsigCV algorithm 14 ; it calculates False-discovery rates ( q -values), then genes with q ⩽ 0.1 were identified as significantly mutated that yielded select 14 mutated genes from MutsigCV output for this study. These genes are listed in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…The outputs of CNNs are fed to an integration layer that utilizes majority votes to predict the model's output. 14 Another work involving SOM was introduced by Jansen et al and named SOMatic. SOMatic is a gene regulatory network that integrates scRNA-seq and scATAC-seq data by assembling a self-organizing map (SOM) for each dataset to distinguish genes and chromatin that might alter over time.…”

Section: Introductionmentioning

confidence: 99%

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Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network

et al. 2022

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Introduction: Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Various promising integrative approaches have been utilized to analyze multi-omics data for biomedical applications, including disease prediction and disease subtypes, biomarker prediction, and others. Methods: In this paper, we introduce a multi-omics data integration method that is constructed using the combination of gene similarity network (GSN) based on uniform manifold approximation and projection (UMAP) and convolutional neural networks (CNNs). The method utilizes UMAP to embed gene expression, DNA methylation, and copy number alteration (CNA) to a lower dimension creating two-dimensional RGB images. Gene expression is used as a reference to construct the GSN and then integrate other omics data with the gene expression for better prediction. We used CNNs to predict the Gleason score levels of prostate cancer patients and the tumor stage in breast cancer patients. Results: The model proposed near perfection with accuracy above 99% with all other performance measurements at the same level. The proposed model outperformed the state-of-art iSOM-GSN model that constructs the GSN map based on the self-organizing map. Conclusion: The results show that UMAP as an embedding technique can better integrate multi-omics maps into the prediction model than SOM. The proposed model can also be applied to build a multi-omics prediction model for other types of cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network

et al. 2022

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“…iCluster uses a joint latent variable method across multi-omics types to model integrative clustering [2]. Recently, Alkhateeb et al proposed a deep learning method to predict the 5-year interval survival of breast cancer based on multi-omics data integration [3]. Network based clustering methods using integrated multi-omics data have been proposed.…”

Section: Introductionmentioning

confidence: 99%

vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer

Zhu

Deasy

et al. 2022

PLoS ONE

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In this paper, we present a network-based clustering method, called vector Wasserstein clustering (vWCluster), based on the vector-valued Wasserstein distance derived from optimal mass transport (OMT) theory. This approach allows for the natural integration of multi-layer representations of data in a given network from which one derives clusters via a hierarchical clustering approach. In this study, we applied the methodology to multi-omics data from the two largest breast cancer studies. The resultant clusters showed significantly different survival rates in Kaplan-Meier analysis in both datasets. CIBERSORT scores were compared among the identified clusters. Out of the 22 CIBERSORT immune cell types, 9 were commonly significantly different in both datasets, suggesting the difference of tumor immune microenvironment in the clusters. vWCluster can aggregate multi-omics data represented as a vectorial form in a network with multiple layers, taking into account the concordant effect of heterogeneous data, and further identify subgroups of tumors in terms of mortality.

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An Attention-Driven Multi-label Image Classification with Semantic Embedding and Graph Convolutional Networks

Sun

Ding

et al. 2022

Cogn Comput

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The problem of traffic congestion not only causes a large amount of economic losses, but also seriously endangers the urban environment. Predicting traffic congestion has important practical significance. So far, most studies have been based on historical data from sensors placed on different roads to predict future traffic flow and speed, to analyze the traffic congestion conditions of a certain road segment. However, due to the fixed position of sensors, it is difficult to mine new information. On the other hand, vehicle trajectory data is more flexible and can extract traffic information as needed. Therefore, we proposed a new traffic congestion prediction model -Multi Adjacency relationship Attention Graph Convolutional Networks(MA2GCN). This model transformed vehicle trajectory data into graph structured data in grid form, and proposed a vehicle entry and exit matrix based on the mobility between different grids. At the same time, in order to improve the performance of the model, this paper also built a new adaptive adjacency matrix generation method and adjacency matrix attention module. This model mainly used gated temporal convolution and graph convolution to extract temporal and spatial information, respectively. Compared with multiple baselines, our model achieved the best performance on Shanghai taxi GPS trajectory dataset. The code is available at https://github.com/zachysun/Taxi Traffic Benchmark.

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Deep Learning Approach for Breast Cancer InClust 5 Prediction based on Multiomics Data Integration

Cited by 9 publications

References 16 publications

Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network

Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network

vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer

An Attention-Driven Multi-label Image Classification with Semantic Embedding and Graph Convolutional Networks

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