Multi-omics integration method based on attention deep learning network for biomedical data classification
•Proposed a novel end-to-end Multi-omics attention deep learning network (MOADLN) for biomedical data classification.•MOADLN jointly explores the correlation between patients in intra-omics and the correlation of cross-omics in the label space.•Construct the patient correlation via self-attention in...
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| Veröffentlicht in: | Computer methods and programs in biomedicine Jg. 231; S. 107377 |
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| Sprache: | Englisch |
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Elsevier B.V
01.04.2023
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| ISSN: | 0169-2607, 1872-7565, 1872-7565 |
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| Abstract | •Proposed a novel end-to-end Multi-omics attention deep learning network (MOADLN) for biomedical data classification.•MOADLN jointly explores the correlation between patients in intra-omics and the correlation of cross-omics in the label space.•Construct the patient correlation via self-attention in the latent feature space for omics-specific feature learning.
Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics.
We propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction.
We demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets.
MOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data. |
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| AbstractList | Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics.BACKGROUND AND OBJECTIVEIntegrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics.We propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction.METHODSWe propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction.We demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets.RESULTSWe demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets.MOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data.CONCLUSIONSMOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data. •Proposed a novel end-to-end Multi-omics attention deep learning network (MOADLN) for biomedical data classification.•MOADLN jointly explores the correlation between patients in intra-omics and the correlation of cross-omics in the label space.•Construct the patient correlation via self-attention in the latent feature space for omics-specific feature learning. Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics. We propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction. We demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets. MOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data. Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics. We propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction. We demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets. MOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data. |
| ArticleNumber | 107377 |
| Author | Zhang, Zhiyuan Li, Enshuo Meng, Ao Cheng, Lei Gong, Ping Zhang, Longzhen Chen, Jie |
| Author_xml | – sequence: 1 givenname: Ping surname: Gong fullname: Gong, Ping email: gongping@xzhmu.edu.cn organization: School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China – sequence: 2 givenname: Lei surname: Cheng fullname: Cheng, Lei organization: School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China – sequence: 3 givenname: Zhiyuan surname: Zhang fullname: Zhang, Zhiyuan organization: School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China – sequence: 4 givenname: Ao surname: Meng fullname: Meng, Ao organization: School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China – sequence: 5 givenname: Enshuo surname: Li fullname: Li, Enshuo organization: School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China – sequence: 6 givenname: Jie surname: Chen fullname: Chen, Jie organization: Department of Radiation Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, CN, China – sequence: 7 givenname: Longzhen surname: Zhang fullname: Zhang, Longzhen organization: Department of Radiation Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, CN, China |
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| Cites_doi | 10.1080/15384047.2020.1863120 10.1016/j.inffus.2020.09.006 10.2147/OTT.S189265 10.1093/nar/gkv1507 10.31768/2312-8852.2017.39(2):145-150 10.1016/j.cell.2019.10.038 10.1002/sim.6732 10.2174/156720512801322573 10.3389/fgene.2019.00166 10.1093/bfgp/ely030 10.3390/biom11111591 10.1186/1756-0381-6-23 10.1016/j.csbj.2021.04.060 10.1016/j.cmpb.2021.106252 10.1186/1471-2105-13-326 10.3390/cancers14194763 10.1016/j.future.2020.11.001 10.1017/S1041610209990068 10.3389/fgene.2018.00477 10.1093/bioinformatics/btab551 10.1038/nature11412 10.1186/s12911-020-01225-8 10.1038/sdata.2018.142 10.3389/fnagi.2022.955461 10.1016/j.inffus.2021.07.010 10.1016/j.future.2019.09.047 10.3233/JAD-2010-101348 10.1016/j.cmpb.2022.107109 10.1200/JCO.2008.18.1370 10.1093/bib/bbab454 10.3233/JAD-161179 10.1093/bioinformatics/btab109 10.1093/bioinformatics/btz318 10.3390/genes11121436 10.1016/j.compbiomed.2020.103761 10.1093/bioinformatics/bty1054 10.3233/JAD-180940 10.1016/j.cmpb.2020.105337 10.1186/s13045-019-0700-2 10.1007/s10238-021-00757-1 10.1074/jbc.RA119.010710 10.3390/genes10030200 10.1158/1078-0432.CCR-17-0853 10.4103/jcrt.jcrt_280_21 10.1016/j.csbj.2021.06.030 10.1186/s12911-020-1114-3 10.1517/14728222.2016.1135132 10.1093/bioinformatics/btab608 |
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| Keywords | Biomedical data classification Deep learning Attention mechanism Multi-omics integration |
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| References | Srivastava (bib0030) 2021; 11 Van De Wiel, Lien, V erlaat, van Wieringen, Wilting (bib0007) 2016; 35 Usama (bib0029) 2019; 190 Huang (bib0003) 2019; 10 Adossa (bib0023) 2021; 19 Singh (bib0005) 2019; 35 Chaudhary (bib0018) 2018; 24 Colaprico (bib0043) 2016; 44 Zhang (bib0036) 2020 Shi (bib0059) 2019; 12 Ma (bib0044) 2020; 121 Qiu, Zheng, Zhu, Huang (bib0006) 2021 Picard (bib0022) 22 Jun. 2021; 19 Wang, Ding, Tao, Liu, Fu (bib0037) 2019 Ma (bib0047) 2009; 21 Parker (bib0041) 2009; 27 Mustafa Abualsaud (bib0031) 2019 Hassan (bib0016) 2022; 77 Wang (bib0060) 2022; 22 Ahmed (bib0015) 2022; 38 Wang, Shao, Huang (bib0026) 2020 Li (bib0053) 2019; 12 Zhang (bib0048) 2022 Qiu, Cheng, Wang (bib0028) 2022 Zhang (bib0020) 2018; 9 Kim, Li, Dudek, Ritchie (bib0004) 2013; 6 Li, Ma, Leng (bib0027) 2022 Boscher (bib0049) 2019; 68 Martinez-Garcia (bib0046) 2010; 22 Zhang (bib0032) 2022; 14 Fisher, Rudin, Dominici (bib0045) 2019; 20 Han, Du, Zhang (bib0054) 2017; 21 Zhu (bib0035) 2015 Deryusheva (bib0052) 2017 Bennett, Schneider, Arvanitakis, S Wilson (bib0038) 2012; 9 Yang, Chen, Li, Wang (bib0025) 2021 Ghiam (bib0051) 2022; 14 Günther (bib0002) 2012; 13 Kumar (bib0013) 2022; 226 Tan, Huang, Hu (bib0001) 2020; 20 Piccialli, Di Somma (bib0011) 2021 Tong, Mitchel, Chatlin, Wang (bib0024) 2020; 20 Riancho (bib0050) 2017; 57 Koboldt (bib0042) 2012; 490 Kang (bib0017) 2022 Li (bib0058) 2021; 17 Wong, Fortino, Abbott (bib0012) 2020; 110 Kan (bib0057) 2020; 11 Sharifi-Noghabi (bib0021) 2019; 35 Qiu, Cheng, Wang (bib0019) 2021; 208 Zhang (bib0014) 2019; 18 Lv, Dong, Gao (bib0056) 2021; 22 Tao, Song, Du (bib0009) 2019; 10 Yang, Wang, Liu, Zhao, Li (bib0034) 2021; 37 De Jager (bib0039) 2018; 5 Gao, Zhu, Dong, Shi, Chen, Song, Huang, Li, Dong, Zhou, Liu, Ma, Wang, Zhou, Liu, Boja, Robles, Ma, Wang, Li, Ding, Wen, Zhang, Rodriguez, Gao, Zhou, Fan (bib0010) 2019; 179 Xu, Peng, Xiatian Zhu, and David A Clifton., Multimodal learning with transformers: a survey, arXiv preprint arXiv:2206.06488 (2022). Hodes, Buckholtz (bib0040) 2016; 20 Gasparyan (bib0055) 2020; 295 Li (bib0008) 2020; 189 Qiu (10.1016/j.cmpb.2023.107377_bib0028) 2022 Fisher (10.1016/j.cmpb.2023.107377_bib0045) 2019; 20 Han (10.1016/j.cmpb.2023.107377_bib0054) 2017; 21 Van De Wiel (10.1016/j.cmpb.2023.107377_bib0007) 2016; 35 Günther (10.1016/j.cmpb.2023.107377_bib0002) 2012; 13 Wang (10.1016/j.cmpb.2023.107377_bib0026) 2020 Kang (10.1016/j.cmpb.2023.107377_bib0017) 2022 Zhang (10.1016/j.cmpb.2023.107377_bib0014) 2019; 18 Yang (10.1016/j.cmpb.2023.107377_bib0034) 2021; 37 Qiu (10.1016/j.cmpb.2023.107377_bib0019) 2021; 208 Lv (10.1016/j.cmpb.2023.107377_bib0056) 2021; 22 Wang (10.1016/j.cmpb.2023.107377_bib0060) 2022; 22 Deryusheva (10.1016/j.cmpb.2023.107377_bib0052) 2017 Gasparyan (10.1016/j.cmpb.2023.107377_bib0055) 2020; 295 Mustafa Abualsaud (10.1016/j.cmpb.2023.107377_bib0031) 2019 10.1016/j.cmpb.2023.107377_bib0033 Colaprico (10.1016/j.cmpb.2023.107377_bib0043) 2016; 44 Sharifi-Noghabi (10.1016/j.cmpb.2023.107377_bib0021) 2019; 35 Shi (10.1016/j.cmpb.2023.107377_bib0059) 2019; 12 Singh (10.1016/j.cmpb.2023.107377_bib0005) 2019; 35 Picard (10.1016/j.cmpb.2023.107377_bib0022) 2021; 19 Kim (10.1016/j.cmpb.2023.107377_bib0004) 2013; 6 Li (10.1016/j.cmpb.2023.107377_bib0008) 2020; 189 Piccialli (10.1016/j.cmpb.2023.107377_bib0011) 2021 Adossa (10.1016/j.cmpb.2023.107377_bib0023) 2021; 19 Hassan (10.1016/j.cmpb.2023.107377_bib0016) 2022; 77 Huang (10.1016/j.cmpb.2023.107377_bib0003) 2019; 10 Tong (10.1016/j.cmpb.2023.107377_bib0024) 2020; 20 Tan (10.1016/j.cmpb.2023.107377_bib0001) 2020; 20 Tao (10.1016/j.cmpb.2023.107377_bib0009) 2019; 10 Zhu (10.1016/j.cmpb.2023.107377_bib0035) 2015 Li (10.1016/j.cmpb.2023.107377_bib0027) 2022 Hodes (10.1016/j.cmpb.2023.107377_bib0040) 2016; 20 Wang (10.1016/j.cmpb.2023.107377_bib0037) 2019 Li (10.1016/j.cmpb.2023.107377_bib0053) 2019; 12 Srivastava (10.1016/j.cmpb.2023.107377_bib0030) 2021; 11 Ma (10.1016/j.cmpb.2023.107377_bib0044) 2020; 121 Zhang (10.1016/j.cmpb.2023.107377_bib0048) 2022 Qiu (10.1016/j.cmpb.2023.107377_bib0006) 2021 Ghiam (10.1016/j.cmpb.2023.107377_bib0051) 2022; 14 Zhang (10.1016/j.cmpb.2023.107377_bib0020) 2018; 9 Riancho (10.1016/j.cmpb.2023.107377_bib0050) 2017; 57 Ma (10.1016/j.cmpb.2023.107377_bib0047) 2009; 21 Boscher (10.1016/j.cmpb.2023.107377_bib0049) 2019; 68 Zhang (10.1016/j.cmpb.2023.107377_bib0032) 2022; 14 Wong (10.1016/j.cmpb.2023.107377_bib0012) 2020; 110 Usama (10.1016/j.cmpb.2023.107377_bib0029) 2019; 190 Ahmed (10.1016/j.cmpb.2023.107377_bib0015) 2022; 38 Martinez-Garcia (10.1016/j.cmpb.2023.107377_bib0046) 2010; 22 Kan (10.1016/j.cmpb.2023.107377_bib0057) 2020; 11 Kumar (10.1016/j.cmpb.2023.107377_bib0013) 2022; 226 Gao (10.1016/j.cmpb.2023.107377_bib0010) 2019; 179 Li (10.1016/j.cmpb.2023.107377_bib0058) 2021; 17 Koboldt (10.1016/j.cmpb.2023.107377_bib0042) 2012; 490 Parker (10.1016/j.cmpb.2023.107377_bib0041) 2009; 27 Chaudhary (10.1016/j.cmpb.2023.107377_bib0018) 2018; 24 De Jager (10.1016/j.cmpb.2023.107377_bib0039) 2018; 5 Yang (10.1016/j.cmpb.2023.107377_bib0025) 2021 Zhang (10.1016/j.cmpb.2023.107377_bib0036) 2020 Bennett (10.1016/j.cmpb.2023.107377_bib0038) 2012; 9 |
| References_xml | – year: 2017 ident: bib0052 article-title: Genome-wide association study of loss of heterozygosity and metastasis-free survival in breast cancer patients publication-title: Exp. Oncol. – volume: 19 start-page: 2588 year: 2021 end-page: 2596 ident: bib0023 article-title: Computational strategies for single-cell multi-omics integration publication-title: Comput. Struct. Biotechnol. J. – volume: 20 start-page: 129 year: 2020 ident: bib0001 article-title: A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction publication-title: BMC Med. Inform. Decis. Mak. – volume: 24 start-page: 1248 year: 2018 end-page: 1259 ident: bib0018 article-title: Deep learning–based multi-omics integration robustly predicts survival in liver cancerusing deep learning to predict liver cancer prognosis publication-title: Clin. Cancer Res. – volume: 189 year: 2020 ident: bib0008 article-title: Bregmannian consensus clustering for cancer subtypes analysis publication-title: Comput. Methods Programs Biomed. – year: 2022 ident: bib0027 article-title: MoGCN: a multi-omics integration method based on graph convolutional network for cancer subtype analysis publication-title: Front. Genet. – start-page: 111 year: 2021 end-page: 137 ident: bib0011 article-title: A survey on deep learning in medicine: why, how and when? publication-title: Inf. Fusion – volume: 22 start-page: 1181 year: 2010 end-page: 1187 ident: bib0046 article-title: PLA2G3, a gene involved in oxidative stress induced death, is associated with Alzheimer's disease publication-title: J.Alzheimer's Dis. – reference: Xu, Peng, Xiatian Zhu, and David A Clifton., Multimodal learning with transformers: a survey, arXiv preprint arXiv:2206.06488 (2022). – year: 2022 ident: bib0028 article-title: Residual dense attention networks for COVID-19 computed tomography images super-resolution publication-title: IEEE Trans. Cogn. Dev. Syst. – volume: 37 start-page: 4668 year: 2021 end-page: 4676 ident: bib0034 article-title: Phosidn: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information publication-title: Bioinformatics – volume: 21 start-page: 977 year: 2009 end-page: 986 ident: bib0047 article-title: Polymorphisms of the estrogen receptor α (ESR1) gene and the risk of Alzheimer's disease in a southern Chinese community publication-title: Int. Psychogeriat. – start-page: 1402 year: 2022 ident: bib0048 article-title: Integrated network pharmacology and comprehensive bioinformatics identifying the mechanisms and molecular targets of YiZhiQingXin Formula for treatment of comorbidity with Alzheimer's disease and depression publication-title: Front. Pharmacol. – start-page: 6212 year: 2019 end-page: 6221 ident: bib0037 article-title: Generative multi-view human action recognition publication-title: Proceedings of the IEEE International Conference on Computer Vision – volume: 22 start-page: 385 year: 2022 end-page: 392 ident: bib0060 article-title: miR-522 regulates cell proliferation, migration, invasion capacities and acts as a potential biomarker to predict prognosis in triple-negative breast cancer publication-title: Clin. Exp. Med. – volume: 35 start-page: 3055 year: 2019 end-page: 3062 ident: bib0005 article-title: Diablo: an integrative approach for identifying key molecular drivers from multi-omics assays publication-title: Bioinformatics – volume: 295 start-page: 12188 year: 2020 end-page: 12202 ident: bib0055 article-title: Combined p53-and PTEN-deficiency activates expression of mesenchyme homeobox 1 (MEOX1) required for growth of triple-negative breast cancer publication-title: J. Biol. Chem. – volume: 14 year: 2022 ident: bib0051 article-title: Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer's disease publication-title: Front. Aging Neurosci. – volume: 226 year: 2022 ident: bib0013 article-title: A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques publication-title: Comput. Methods Programs Biomed. – volume: 14 start-page: 4763 year: 2022 ident: bib0032 article-title: Transformer for Gene Expression Modeling (T-GEM): an Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions publication-title: Cancers (Basel) – year: 2021 ident: bib0025 article-title: Subtype-gan: a deep learning approach for integrative cancer subtyping of multi-omics data publication-title: Bioinformatics – volume: 10 start-page: 200 year: 2019 ident: bib0009 article-title: Classifying breast cancer subtypes using multiple kernel learning based on omics data publication-title: Genes (Basel) – start-page: 200 year: 2021 end-page: 208 ident: bib0006 article-title: Multiple improved residual networks for medical image super-resolution publication-title: Future Gener. Comput. Syst. – volume: 10 start-page: 166 year: 2019 ident: bib0003 article-title: Salmon: survival analysis learning with multi-omics neural networks on breast cancer publication-title: Front. Genet. – volume: 13 start-page: 1 year: 2012 end-page: 18 ident: bib0002 article-title: A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers publication-title: BMC Bioinformatics – volume: 121 year: 2020 ident: bib0044 article-title: Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data publication-title: Comput. Biol. Med. – volume: 17 start-page: 749 year: 2021 ident: bib0058 article-title: Detection significance of miR-3662, miR-146a, and miR-1290 in serum exosomes of breast cancer patients publication-title: J. Cancer Res. Ther. – volume: 35 start-page: 368 year: 2016 end-page: 381 ident: bib0007 article-title: Better prediction by use of co-data: adaptive group-regularized ridge regression publication-title: Stat. Med. – volume: 38 start-page: 179 year: 2022 end-page: 186 ident: bib0015 article-title: Multi-omics data integration by generative adversarial network publication-title: Bioinformatics – volume: 208 year: 2021 ident: bib0019 article-title: Gradual back-projection residual attention network for magnetic resonance image super-resolution publication-title: Comput. Methods Programs Biomed. – volume: 57 start-page: 483 year: 2017 end-page: 491 ident: bib0050 article-title: MicroRNA profile in patients with Alzheimer's disease: analysis of miR-9-5p and miR-598 in raw and exosome enriched cerebrospinal fluid samples publication-title: J. Alzheimer's Dis. – volume: 179 start-page: 1240 year: 2019 ident: bib0010 article-title: Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma publication-title: Cell – volume: 20 start-page: 225 year: 2020 ident: bib0024 article-title: Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis publication-title: BMC Med. Inform. Decis. Mak. – volume: 12 start-page: 1 year: 2019 end-page: 18 ident: bib0053 article-title: SPAG5 upregulation contributes to enhanced c-MYC transcriptional activity via interaction with c-MYC binding protein in triple-negative breast cancer publication-title: J. Hematol. Oncol. – start-page: 255 year: 2015 end-page: 262 ident: bib0035 article-title: Multi-view classification for identification of Alzheimer's disease publication-title: International Workshop on Machine Learning in Medical Imaging – volume: 22 start-page: 248 year: 2021 end-page: 256 ident: bib0056 article-title: Long non-coding RNA TDRG1 facilitates cell proliferation, migration and invasion in breast cancer via targeting miR-214-5p/CLIC4 axis publication-title: Cancer Biol. Ther. – volume: 190 year: 2019 ident: bib0029 article-title: Self-attention based recurrent convolutional neural network for disease prediction using healthcare data publication-title: Comput. Methods Programs Biomed. – volume: 27 start-page: 1160 year: 2009 end-page: 1167 ident: bib0041 article-title: Supervised risk predictor of breast cancer based on intrinsic subtypes publication-title: J. Clin. Oncol. – volume: 20 start-page: 1 year: 2019 end-page: 81 ident: bib0045 article-title: All Models are Wrong, but Many are Useful: learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously publication-title: J. Mach. Learn. Res. – volume: 490 start-page: 61 year: 2012 ident: bib0042 article-title: Comprehensive molecular portraits of human breast tumours publication-title: Nature – volume: 20 start-page: 389 year: 2016 end-page: 391 ident: bib0040 article-title: Accelerating medicines partnership: alzheimer's disease (amp-ad) knowledge portal aids alzheimer's drug discovery through open data sharing publication-title: Expert Opin. Ther. Targets – volume: 44 year: 2016 ident: bib0043 article-title: Tcgabiolinks: an r/bioconductor package for integrative analysis of tcga data publication-title: Nucleic Acids Res. – year: 2020 ident: bib0036 article-title: CMC: a consensus multi-view clustering model for predicting Alzheimer's disease progression publication-title: Comput. Methods Programs Biomed. – volume: 5 year: 2018 ident: bib0039 article-title: A multi-omic atlas of the human frontal cortex for aging and alzheimer's disease research publication-title: Sci. Data – start-page: bbab454 year: 2022 ident: bib0017 article-title: A roadmap for multi-omics data integration using deep learning publication-title: Brief. Bioinformatics – volume: 18 start-page: 41 year: 2019 end-page: 57 ident: bib0014 article-title: Deep learning in omics: a survey and guideline publication-title: Brief. Funct. Genomics – year: 2019 ident: bib0031 article-title: Proceedings of the 28th acm international conference on information and knowledge management publication-title: Proceedings of the 28th ACM International Conference on Information and Knowledge Management – volume: 9 start-page: 628 year: 2012 end-page: 645 ident: bib0038 article-title: Overview and findings from the religious orders study publication-title: Curr. Alzheimer Re.s – volume: 6 start-page: 1 year: 2013 end-page: 14 ident: bib0004 article-title: Athena: identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network publication-title: BioData Min. – volume: 21 start-page: 2405 year: 2017 end-page: 2412 ident: bib0054 article-title: Bioinformatic analysis of prognostic value of ARAP3 in breast cancer and the associated signaling pathways publication-title: Eur. Rev. Med. Pharmacol. Sci. – volume: 12 start-page: 1979 year: 2019 ident: bib0059 article-title: Construction of prognostic microRNA signature for human invasive breast cancer by integrated analysis publication-title: Onco Targets Ther. – volume: 35 start-page: i501 year: 2019 end-page: i509 ident: bib0021 article-title: MOLI: multi-omics late integration with deep neural networks for drug response prediction publication-title: Bioinformatics – volume: 110 start-page: 802 year: 2020 end-page: 811 ident: bib0012 article-title: Deep learning-based cardiovascular image diagnosis: a promising challenge publication-title: Future Gener. Comput. Syst. – volume: 9 start-page: 477 year: 2018 ident: bib0020 article-title: Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma publication-title: Front. Genet. – volume: 11 start-page: 1436 year: 2020 ident: bib0057 article-title: Comprehensive transcriptomic analysis identifies ST8SIA1 as a survival-related Sialyltransferase gene in breast cancer publication-title: Genes (Basel) – volume: 68 start-page: 1243 year: 2019 end-page: 1255 ident: bib0049 article-title: Copy number variants in miR-138 as a potential risk factor for early-onset Alzheimer's disease publication-title: J. Alzheimer's Dis. – year: 2020 ident: bib0026 article-title: MORONET: Multi-omics Integration Via Graph Convolutional Networks for Biomedical Data Classification – volume: 77 start-page: 70 year: 2022 end-page: 80 ident: bib0016 article-title: Early detection of cardiovascular autonomic neuropathy: a multi-class classification model based on feature selection and deep learning feature fusion publication-title: Inf. Fusion – volume: 19 start-page: 3735 year: 22 Jun. 2021 end-page: 3746 ident: bib0022 article-title: Integration strategies of multi-omics data for machine learning analysis publication-title: Comput. Struct. Biotechnol. J. – volume: 11 start-page: 1591 year: 2021 ident: bib0030 article-title: Self-attention-based models for the extraction of molecular interactions from biological texts publication-title: Biomolecules – year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0031 article-title: Proceedings of the 28th acm international conference on information and knowledge management – volume: 22 start-page: 248 issue: 3 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0056 article-title: Long non-coding RNA TDRG1 facilitates cell proliferation, migration and invasion in breast cancer via targeting miR-214-5p/CLIC4 axis publication-title: Cancer Biol. Ther. doi: 10.1080/15384047.2020.1863120 – start-page: 111 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0011 article-title: A survey on deep learning in medicine: why, how and when? publication-title: Inf. Fusion doi: 10.1016/j.inffus.2020.09.006 – volume: 12 start-page: 1979 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0059 article-title: Construction of prognostic microRNA signature for human invasive breast cancer by integrated analysis publication-title: Onco Targets Ther. doi: 10.2147/OTT.S189265 – volume: 44 issue: 8 year: 2016 ident: 10.1016/j.cmpb.2023.107377_bib0043 article-title: Tcgabiolinks: an r/bioconductor package for integrative analysis of tcga data publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv1507 – year: 2017 ident: 10.1016/j.cmpb.2023.107377_bib0052 article-title: Genome-wide association study of loss of heterozygosity and metastasis-free survival in breast cancer patients publication-title: Exp. Oncol. doi: 10.31768/2312-8852.2017.39(2):145-150 – volume: 179 start-page: 1240 issue: 5 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0010 article-title: Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma publication-title: Cell doi: 10.1016/j.cell.2019.10.038 – ident: 10.1016/j.cmpb.2023.107377_bib0033 – start-page: 1402 year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0048 article-title: Integrated network pharmacology and comprehensive bioinformatics identifying the mechanisms and molecular targets of YiZhiQingXin Formula for treatment of comorbidity with Alzheimer's disease and depression publication-title: Front. Pharmacol. – volume: 21 start-page: 2405 issue: 10 year: 2017 ident: 10.1016/j.cmpb.2023.107377_bib0054 article-title: Bioinformatic analysis of prognostic value of ARAP3 in breast cancer and the associated signaling pathways publication-title: Eur. Rev. Med. Pharmacol. Sci. – volume: 35 start-page: 368 year: 2016 ident: 10.1016/j.cmpb.2023.107377_bib0007 article-title: Better prediction by use of co-data: adaptive group-regularized ridge regression publication-title: Stat. Med. doi: 10.1002/sim.6732 – volume: 9 start-page: 628 year: 2012 ident: 10.1016/j.cmpb.2023.107377_bib0038 article-title: Overview and findings from the religious orders study publication-title: Curr. Alzheimer Re.s doi: 10.2174/156720512801322573 – volume: 10 start-page: 166 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0003 article-title: Salmon: survival analysis learning with multi-omics neural networks on breast cancer publication-title: Front. Genet. doi: 10.3389/fgene.2019.00166 – volume: 18 start-page: 41 issue: 1 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0014 article-title: Deep learning in omics: a survey and guideline publication-title: Brief. Funct. Genomics doi: 10.1093/bfgp/ely030 – volume: 11 start-page: 1591 issue: 11 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0030 article-title: Self-attention-based models for the extraction of molecular interactions from biological texts publication-title: Biomolecules doi: 10.3390/biom11111591 – start-page: 255 year: 2015 ident: 10.1016/j.cmpb.2023.107377_bib0035 article-title: Multi-view classification for identification of Alzheimer's disease – volume: 6 start-page: 1 issue: 1 year: 2013 ident: 10.1016/j.cmpb.2023.107377_bib0004 article-title: Athena: identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network publication-title: BioData Min. doi: 10.1186/1756-0381-6-23 – volume: 19 start-page: 2588 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0023 article-title: Computational strategies for single-cell multi-omics integration publication-title: Comput. Struct. Biotechnol. J. doi: 10.1016/j.csbj.2021.04.060 – volume: 208 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0019 article-title: Gradual back-projection residual attention network for magnetic resonance image super-resolution publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2021.106252 – year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0028 article-title: Residual dense attention networks for COVID-19 computed tomography images super-resolution publication-title: IEEE Trans. Cogn. Dev. Syst. – volume: 13 start-page: 1 year: 2012 ident: 10.1016/j.cmpb.2023.107377_bib0002 article-title: A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-13-326 – volume: 14 start-page: 4763 issue: 19 year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0032 article-title: Transformer for Gene Expression Modeling (T-GEM): an Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions publication-title: Cancers (Basel) doi: 10.3390/cancers14194763 – year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0026 – start-page: 200 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0006 article-title: Multiple improved residual networks for medical image super-resolution publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2020.11.001 – volume: 21 start-page: 977 issue: 5 year: 2009 ident: 10.1016/j.cmpb.2023.107377_bib0047 article-title: Polymorphisms of the estrogen receptor α (ESR1) gene and the risk of Alzheimer's disease in a southern Chinese community publication-title: Int. Psychogeriat. doi: 10.1017/S1041610209990068 – volume: 9 start-page: 477 year: 2018 ident: 10.1016/j.cmpb.2023.107377_bib0020 article-title: Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma publication-title: Front. Genet. doi: 10.3389/fgene.2018.00477 – volume: 37 start-page: 4668 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0034 article-title: Phosidn: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab551 – volume: 490 start-page: 61 year: 2012 ident: 10.1016/j.cmpb.2023.107377_bib0042 article-title: Comprehensive molecular portraits of human breast tumours publication-title: Nature doi: 10.1038/nature11412 – start-page: 6212 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0037 article-title: Generative multi-view human action recognition – volume: 20 start-page: 225 year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0024 article-title: Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis publication-title: BMC Med. Inform. Decis. Mak. doi: 10.1186/s12911-020-01225-8 – volume: 5 year: 2018 ident: 10.1016/j.cmpb.2023.107377_bib0039 article-title: A multi-omic atlas of the human frontal cortex for aging and alzheimer's disease research publication-title: Sci. Data doi: 10.1038/sdata.2018.142 – volume: 14 year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0051 article-title: Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer's disease publication-title: Front. Aging Neurosci. doi: 10.3389/fnagi.2022.955461 – volume: 77 start-page: 70 year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0016 article-title: Early detection of cardiovascular autonomic neuropathy: a multi-class classification model based on feature selection and deep learning feature fusion publication-title: Inf. Fusion doi: 10.1016/j.inffus.2021.07.010 – volume: 110 start-page: 802 year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0012 article-title: Deep learning-based cardiovascular image diagnosis: a promising challenge publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.09.047 – volume: 22 start-page: 1181 issue: 4 year: 2010 ident: 10.1016/j.cmpb.2023.107377_bib0046 article-title: PLA2G3, a gene involved in oxidative stress induced death, is associated with Alzheimer's disease publication-title: J.Alzheimer's Dis. doi: 10.3233/JAD-2010-101348 – volume: 226 year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0013 article-title: A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2022.107109 – volume: 27 start-page: 1160 issue: 8 year: 2009 ident: 10.1016/j.cmpb.2023.107377_bib0041 article-title: Supervised risk predictor of breast cancer based on intrinsic subtypes publication-title: J. Clin. Oncol. doi: 10.1200/JCO.2008.18.1370 – start-page: bbab454 year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0017 article-title: A roadmap for multi-omics data integration using deep learning publication-title: Brief. Bioinformatics doi: 10.1093/bib/bbab454 – volume: 57 start-page: 483 issue: 2 year: 2017 ident: 10.1016/j.cmpb.2023.107377_bib0050 article-title: MicroRNA profile in patients with Alzheimer's disease: analysis of miR-9-5p and miR-598 in raw and exosome enriched cerebrospinal fluid samples publication-title: J. Alzheimer's Dis. doi: 10.3233/JAD-161179 – year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0025 article-title: Subtype-gan: a deep learning approach for integrative cancer subtyping of multi-omics data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab109 – year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0027 article-title: MoGCN: a multi-omics integration method based on graph convolutional network for cancer subtype analysis publication-title: Front. Genet. – volume: 35 start-page: i501 issue: 14 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0021 article-title: MOLI: multi-omics late integration with deep neural networks for drug response prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz318 – volume: 11 start-page: 1436 issue: 12 year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0057 article-title: Comprehensive transcriptomic analysis identifies ST8SIA1 as a survival-related Sialyltransferase gene in breast cancer publication-title: Genes (Basel) doi: 10.3390/genes11121436 – volume: 20 start-page: 1 issue: 177 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0045 article-title: All Models are Wrong, but Many are Useful: learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously publication-title: J. Mach. Learn. Res. – volume: 121 year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0044 article-title: Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.103761 – volume: 35 start-page: 3055 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0005 article-title: Diablo: an integrative approach for identifying key molecular drivers from multi-omics assays publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty1054 – volume: 68 start-page: 1243 issue: 3 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0049 article-title: Copy number variants in miR-138 as a potential risk factor for early-onset Alzheimer's disease publication-title: J. Alzheimer's Dis. doi: 10.3233/JAD-180940 – volume: 189 year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0008 article-title: Bregmannian consensus clustering for cancer subtypes analysis publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2020.105337 – volume: 12 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0053 article-title: SPAG5 upregulation contributes to enhanced c-MYC transcriptional activity via interaction with c-MYC binding protein in triple-negative breast cancer publication-title: J. Hematol. Oncol. doi: 10.1186/s13045-019-0700-2 – year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0036 article-title: CMC: a consensus multi-view clustering model for predicting Alzheimer's disease progression publication-title: Comput. Methods Programs Biomed. – volume: 22 start-page: 385 issue: 3 year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0060 article-title: miR-522 regulates cell proliferation, migration, invasion capacities and acts as a potential biomarker to predict prognosis in triple-negative breast cancer publication-title: Clin. Exp. Med. doi: 10.1007/s10238-021-00757-1 – volume: 190 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0029 article-title: Self-attention based recurrent convolutional neural network for disease prediction using healthcare data publication-title: Comput. Methods Programs Biomed. – volume: 295 start-page: 12188 issue: 34 year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0055 article-title: Combined p53-and PTEN-deficiency activates expression of mesenchyme homeobox 1 (MEOX1) required for growth of triple-negative breast cancer publication-title: J. Biol. Chem. doi: 10.1074/jbc.RA119.010710 – volume: 10 start-page: 200 issue: 3 year: 2019 ident: 10.1016/j.cmpb.2023.107377_bib0009 article-title: Classifying breast cancer subtypes using multiple kernel learning based on omics data publication-title: Genes (Basel) doi: 10.3390/genes10030200 – volume: 24 start-page: 1248 issue: 6 year: 2018 ident: 10.1016/j.cmpb.2023.107377_bib0018 article-title: Deep learning–based multi-omics integration robustly predicts survival in liver cancerusing deep learning to predict liver cancer prognosis publication-title: Clin. Cancer Res. doi: 10.1158/1078-0432.CCR-17-0853 – volume: 17 start-page: 749 issue: 3 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0058 article-title: Detection significance of miR-3662, miR-146a, and miR-1290 in serum exosomes of breast cancer patients publication-title: J. Cancer Res. Ther. doi: 10.4103/jcrt.jcrt_280_21 – volume: 19 start-page: 3735 year: 2021 ident: 10.1016/j.cmpb.2023.107377_bib0022 article-title: Integration strategies of multi-omics data for machine learning analysis publication-title: Comput. Struct. Biotechnol. J. doi: 10.1016/j.csbj.2021.06.030 – volume: 20 start-page: 129 issue: S3 year: 2020 ident: 10.1016/j.cmpb.2023.107377_bib0001 article-title: A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction publication-title: BMC Med. Inform. Decis. Mak. doi: 10.1186/s12911-020-1114-3 – volume: 20 start-page: 389 year: 2016 ident: 10.1016/j.cmpb.2023.107377_bib0040 article-title: Accelerating medicines partnership: alzheimer's disease (amp-ad) knowledge portal aids alzheimer's drug discovery through open data sharing publication-title: Expert Opin. Ther. Targets doi: 10.1517/14728222.2016.1135132 – volume: 38 start-page: 179 issue: 1 year: 2022 ident: 10.1016/j.cmpb.2023.107377_bib0015 article-title: Multi-omics data integration by generative adversarial network publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab608 |
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| SubjectTerms | Algorithms Attention mechanism Biomedical data classification Computational Biology - methods Deep Learning Humans MicroRNAs Multi-omics integration Multiomics |
| Title | Multi-omics integration method based on attention deep learning network for biomedical data classification |
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