references.bib

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@article{wang_similarity_2014,
	title = {Similarity network fusion for aggregating data types on a genomic scale},
	volume = {11},
	copyright = {2014 Springer Nature America, Inc.},
	issn = {1548-7105},
	url = {https://www.nature.com/articles/nmeth.2810},
	doi = {10.1038/nmeth.2810},
	abstract = {Similarity network fusion (SNF) is an approach to integrate multiple data types on the basis of similarity between biological samples rather than individual measurements. The authors demonstrate SNF by constructing patient networks to identify disease subtypes with differential survival profiles.},
	language = {en},
	number = {3},
	urldate = {2024-02-12},
	journal = {Nature Methods},
	author = {Wang, Bo and Mezlini, Aziz M. and Demir, Feyyaz and Fiume, Marc and Tu, Zhuowen and Brudno, Michael and Haibe-Kains, Benjamin and Goldenberg, Anna},
	month = mar,
	year = {2014},
	note = {Number: 3
Publisher: Nature Publishing Group},
	keywords = {Network, Network Integration, Patient Similarity Network},
	pages = {333--337},
}


@article{pai_netdx_2019,
	title = {{netDx}: interpretable patient classification using integrated patient similarity networks},
	volume = {15},
	issn = {1744-4292},
	shorttitle = {{netDx}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423721/},
	doi = {10.15252/msb.20188497},
	abstract = {Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis‐driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine‐learning approaches across most cancer types. Compared to traditional machine‐learning‐based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows.},
	number = {3},
	urldate = {2024-02-12},
	journal = {Molecular Systems Biology},
	author = {Pai, Shraddha and Hui, Shirley and Isserlin, Ruth and Shah, Muhammad A and Kaka, Hussam and Bader, Gary D},
	month = mar,
	year = {2019},
	pmid = {30872331},
	pmcid = {PMC6423721},
	keywords = {Network},
	pages = {e8497},
}

@article{li_mogcn_2022,
	title = {{MoGCN}: {A} {Multi}-{Omics} {Integration} {Method} {Based} on {Graph} {Convolutional} {Network} for {Cancer} {Subtype} {Analysis}},
	volume = {13},
	issn = {1664-8021},
	shorttitle = {{MoGCN}},
	url = {https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.806842},
	abstract = {In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.},
	urldate = {2024-02-12},
	journal = {Frontiers in Genetics},
	author = {Li, Xiao and Ma, Jie and Leng, Ling and Han, Mingfei and Li, Mansheng and He, Fuchu and Zhu, Yunping},
	year = {2022},
	keywords = {Network, Network Integration, Patient Similarity Network},
}

@article{wang_mogonet_2021,
	title = {{MOGONET} integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification},
	volume = {12},
	copyright = {2021 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-021-23774-w},
	doi = {10.1038/s41467-021-23774-w},
	abstract = {To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.},
	language = {en},
	number = {1},
	urldate = {2024-02-12},
	journal = {Nature Communications},
	author = {Wang, Tongxin and Shao, Wei and Huang, Zhi and Tang, Haixu and Zhang, Jie and Ding, Zhengming and Huang, Kun},
	month = jun,
	year = {2021},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Network, Network Integration, Patient Similarity Network},
	pages = {3445},
}

@book{hamilton_graph_2020,
	address = {Cham},
	series = {Synthesis {Lectures} on {Artificial} {Intelligence} and {Machine} {Learning}},
	title = {Graph {Representation} {Learning}},
	isbn = {978-3-031-00460-5 978-3-031-01588-5},
	url = {https://link.springer.com/10.1007/978-3-031-01588-5},
	language = {en},
	urldate = {2024-03-13},
	publisher = {Springer International Publishing},
	author = {Hamilton, William L.},
	year = {2020},
	doi = {10.1007/978-3-031-01588-5},
}

@book{kipf_semi-supervised_2017,
	title = {Semi-{Supervised} {Classification} with {Graph} {Convolutional} {Networks}},
	url = {http://arxiv.org/abs/1609.02907},
	abstract = {We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.},
	language = {en},
	urldate = {2022-09-26},
	publisher = {arXiv},
	author = {Kipf, Thomas N. and Welling, Max},
	month = feb,
	year = {2017},
	note = {arXiv:1609.02907 [cs, stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@inproceedings{hamilton_inductive_2017,
	title = {Inductive {Representation} {Learning} on {Large} {Graphs}},
	volume = {30},
	url = {https://proceedings.neurips.cc/paper_files/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html},
	abstract = {Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings.  Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.},
	urldate = {2024-02-12},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Hamilton, Will and Ying, Zhitao and Leskovec, Jure},
	year = {2017},
	keywords = {Graph Neural Network, Network, Network Integration},
}

@article{yang_subtype-gan_2021,
	title = {Subtype-{GAN}: a deep learning approach for integrative cancer subtyping of multi-omics data},
	volume = {37},
	issn = {1367-4803},
	shorttitle = {Subtype-{GAN}},
	url = {https://doi.org/10.1093/bioinformatics/btab109},
	doi = {10.1093/bioinformatics/btab109},
	abstract = {The discovery of cancer subtyping can help explore cancer pathogenesis, determine clinical actionability in treatment, and improve patients' survival rates. However, due to the diversity and complexity of multi-omics data, it is still challenging to develop integrated clustering algorithms for tumor molecular subtyping.We propose Subtype-GAN, a deep adversarial learning approach based on the multiple-input multiple-output neural network to model the complex omics data accurately. With the latent variables extracted from the neural network, Subtype-GAN uses consensus clustering and the Gaussian Mixture model to identify tumor samples' molecular subtypes. Compared with other state-of-the-art subtyping approaches, Subtype-GAN achieved outstanding performance on the benchmark datasets consisting of ∼4000 TCGA tumors from 10 types of cancer. We found that on the comparison dataset, the clustering scheme of Subtype-GAN is not always similar to that of the deep learning method AE but is identical to that of NEMO, MCCA, VAE and other excellent approaches. Finally, we applied Subtype-GAN to the BRCA dataset and automatically obtained the number of subtypes and the subtype labels of 1031 BRCA tumors. Through the detailed analysis, we found that the identified subtypes are clinically meaningful and show distinct patterns in the feature space, demonstrating the practicality of Subtype-GAN.The source codes, the clustering results of Subtype-GAN across the benchmark datasets are available at https://github.com/haiyang1986/Subtype-GAN.Supplementary data are available at Bioinformatics online.},
	number = {16},
	urldate = {2024-03-15},
	journal = {Bioinformatics},
	author = {Yang, Hai and Chen, Rui and Li, Dongdong and Wang, Zhe},
	month = aug,
	year = {2021},
	pages = {2231--2237},
}

@article{xu_hierarchical_2019,
	title = {A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data},
	volume = {20},
	issn = {1471-2105},
	url = {https://doi.org/10.1186/s12859-019-3116-7},
	doi = {10.1186/s12859-019-3116-7},
	abstract = {Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification.},
	language = {en},
	number = {1},
	urldate = {2024-03-15},
	journal = {BMC Bioinformatics},
	author = {Xu, Jing and Wu, Peng and Chen, Yuehui and Meng, Qingfang and Dawood, Hussain and Dawood, Hassan},
	month = oct,
	year = {2019},
	keywords = {Autoencoder, Cancer subtype classification, Cascade forest, Data integration, Deep learning},
	pages = {527},
}