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Yang P, Chen W, Qiu H. MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 257:108400. [PMID: 39270533 DOI: 10.1016/j.cmpb.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/14/2024] [Accepted: 08/27/2024] [Indexed: 09/15/2024]
Abstract
BACKGROUND AND OBJECTIVE Accurate prognosis prediction for cancer patients plays a significant role in the formulation of treatment strategies, considerably impacting personalized medicine. Recent advancements in this field indicate that integrating information from various modalities, such as genetic and clinical data, and developing multi-modal deep learning models can enhance prediction accuracy. However, most existing multi-modal deep learning methods either overlook patient similarities that benefit prognosis prediction or fail to effectively capture diverse information due to measuring patient similarities from a single perspective. To address these issues, a novel framework called multi-modal multi-view graph convolutional networks (MMGCN) is proposed for cancer prognosis prediction. METHODS Initially, we utilize the similarity network fusion (SNF) algorithm to merge patient similarity networks (PSNs), individually constructed using gene expression, copy number alteration, and clinical data, into a fused PSN for integrating multi-modal information. To capture diverse perspectives of patient similarities, we treat the fused PSN as a multi-view graph by considering each single-edge-type subgraph as a view graph, and propose multi-view graph convolutional networks (GCNs) with a view-level attention mechanism. Moreover, an edge homophily prediction module is designed to alleviate the adverse effects of heterophilic edges on the representation power of GCNs. Finally, comprehensive representations of patient nodes are obtained to predict cancer prognosis. RESULTS Experimental results demonstrate that MMGCN outperforms state-of-the-art baselines on four public datasets, including METABRIC, TCGA-BRCA, TCGA-LGG, and TCGA-LUSC, with the area under the receiver operating characteristic curve achieving 0.827 ± 0.005, 0.805 ± 0.014, 0.925 ± 0.007, and 0.746 ± 0.013, respectively. CONCLUSIONS Our study reveals the effectiveness of the proposed MMGCN, which deeply explores patient similarities related to different modalities from a broad perspective, in enhancing the performance of multi-modal cancer prognosis prediction. The source code is publicly available at https://github.com/ping-y/MMGCN.
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Affiliation(s)
- Ping Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Wengxiang Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Hang Qiu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
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2
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Li M, Cai Y, Zhang M, Deng S, Wang L. NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108291. [PMID: 38909399 DOI: 10.1016/j.cmpb.2024.108291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 05/09/2024] [Accepted: 06/16/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. METHODS NNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. RESULTS The SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. CONCLUSIONS The proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.
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Affiliation(s)
- Min Li
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China.
| | - Yuheng Cai
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China
| | - Mingzhuang Zhang
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China
| | - Shaobo Deng
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China
| | - Lei Wang
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China
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Cortesi M, Liu D, Powell E, Barlow E, Warton K, Ford CE. Accurate Identification of Cancer Cells in Complex Pre-Clinical Models Using a Deep-Learning Neural Network: A Transfection-Free Approach. Adv Biol (Weinh) 2024:e2400034. [PMID: 39133225 DOI: 10.1002/adbi.202400034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 07/07/2024] [Indexed: 08/13/2024]
Abstract
3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment while allowing a tighter control on the culture's composition and experimental conditions. The limited technologies available for the analysis of these models, however, hamper their widespread application. The separation of the contribution of the different cell types, in particular, is a fundamental challenge. In this work, ORACLE (OvaRiAn Cancer ceLl rEcognition) is presented, a deep neural network trained to distinguish between ovarian cancer and healthy cells based on the shape of their nucleus. The extensive validation that are conducted includes multiple cell lines and patient-derived cultures to characterize the effect of all the major potential confounding factors. High accuracy and reliability are maintained throughout the analysis (F1score> 0.9 and Area under the ROC curve -ROC-AUC- score = 0.99) demonstrating ORACLE's effectiveness with this detection and classification task. ORACLE is freely available (https://github.com/MarilisaCortesi/ORACLE/tree/main) and can be used to recognize both ovarian cancer cell lines and primary patient-derived cells. This feature is unique to ORACLE and thus enables for the first time the analysis of in vitro co-cultures comprised solely of patient-derived cells.
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Affiliation(s)
- Marilisa Cortesi
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, 2033, Australia
- Laboratory of Cellular and Molecular Engineering, Department of Electrical Electronic and Information Engineering "G. Marconi", Alma Mater Studiorum-University of Bologna, Cesena, 47521, Italy
| | - Dongli Liu
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, 2033, Australia
| | - Elyse Powell
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, 2033, Australia
| | - Ellen Barlow
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, 2033, Australia
| | - Kristina Warton
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, 2033, Australia
| | - Caroline E Ford
- Gynaecological Cancer Research Group, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Kensington, NSW, 2033, Australia
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Liu A, Zhao Y, Shen H, Ding Z, Deng HW. ResSAT: Enhancing Spatial Transcriptomics Prediction from H&E- Stained Histology Images with Interactive Spot Transformer. RESEARCH SQUARE 2024:rs.3.rs-4707959. [PMID: 39149477 PMCID: PMC11326376 DOI: 10.21203/rs.3.rs-4707959/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Spatial transcriptomics (ST) revolutionizes RNA quantification with high spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. Current methods for predicting spatial gene expression from H&E images often overlook spatial relationships. We introduce ResSAT (Residual networks - Self-Attention Transformer), a framework generating spatially resolved gene expression profiles from H&E images by capturing tissue structures and using a self-attention transformer to enhance prediction.Benchmarking on 10× Visium datasets, ResSAT significantly outperformed existing methods, promising reduced ST profiling costs and rapid acquisition of numerous profiles.
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Yang P, Qiu H, Yang X, Wang L, Wang X. SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 249:108159. [PMID: 38583291 DOI: 10.1016/j.cmpb.2024.108159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/28/2024] [Accepted: 03/29/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND AND OBJECTIVE Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learning and deep learning approaches have been increasingly applied in cancer survival prediction. However, most existing methods inadequately represent and leverage the dependencies among features and fail to sufficiently mine and utilize the comorbidity patterns of CRC. To address these issues, we propose a self-attention-based graph learning (SAGL) framework to improve the postoperative cancer-specific survival prediction for CRC patients. METHODS We present a novel method for constructing dependency graph (DG) to reflect two types of dependencies including comorbidity-comorbidity dependencies and the dependencies between features related to patient characteristics and cancer treatments. This graph is subsequently refined by a disease comorbidity network, which offers a holistic view of comorbidity patterns of CRC. A DG-guided self-attention mechanism is proposed to unearth novel dependencies beyond what DG offers, thus augmenting CRC survival prediction. Finally, each patient will be represented, and these representations will be used for survival prediction. RESULTS The experimental results show that SAGL outperforms state-of-the-art methods on a real-world dataset, with the receiver operating characteristic curve for 3- and 5-year survival prediction achieving 0.849±0.002 and 0.895±0.005, respectively. In addition, the comparison results with different graph neural network-based variants demonstrate the advantages of our DG-guided self-attention graph learning framework. CONCLUSIONS Our study reveals that the potential of the DG-guided self-attention in optimizing feature graph learning which can improve the performance of CRC survival prediction.
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Affiliation(s)
- Ping Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Hang Qiu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Xulin Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xiaodong Wang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, PR China.
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Madhan S, Kalaiselvan A. Omics data classification using constitutive artificial neural network optimized with single candidate optimizer. NETWORK (BRISTOL, ENGLAND) 2024:1-25. [PMID: 38736309 DOI: 10.1080/0954898x.2024.2348726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/23/2024] [Indexed: 05/14/2024]
Abstract
Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using Adaptive variational Bayesian filtering (AVBF) to replace missing values. The pre-processing data is fed to Zebra Optimization Algorithm (ZOA) for dimensionality reduction. Then, the Constitutive Artificial Neural Network (CANN) is employed to classify omics data. The weight parameter is optimized by Single Candidate Optimizer (SCO). The proposed ODC-ZOA-CANN-SCO method attains 25.36%, 21.04%, 22.18%, 26.90%, and 28.12% higher accuracy when analysed to the existing methods like multi-omics data integration utilizing adaptive graph learning and attention mode for patient categorization with biomarker identification (MOD-AGL-AM-PABI), deep learning method depending upon multi-omics data integration to create risk stratification prediction mode for skin cutaneous melanoma (DL-MODI-RSP-SCM), Deep belief network-base model for identifying Alzheimer's disease utilizing multi-omics data (DDN-DAD-MOD), hybrid cancer prediction depending upon multi-omics data and reinforcement learning state action reward state action (HCP-MOD-RL-SARSA), machine learning basis method under omics data including biological knowledge database for cancer clinical endpoint prediction (ML-ODBKD-CCEP) methods, respectively.
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Affiliation(s)
- Subramaniam Madhan
- Department of Computer Science and Engineering, University College of Engineering, Thirukkuvalai (A Constituent College of Anna University Chennai), Nagapattinam, Tamilnadu, India
| | - Anbarasan Kalaiselvan
- Department of Science and Humanities, University College of Engineering, Thirukkuvalai (A Constituent College of Anna University Chennai), Nagapattinam, Tamilnadu, India
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Lan W, Liao H, Chen Q, Zhu L, Pan Y, Chen YPP. DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery. Brief Bioinform 2024; 25:bbae185. [PMID: 38678587 PMCID: PMC11056029 DOI: 10.1093/bib/bbae185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 03/07/2024] [Accepted: 04/09/2024] [Indexed: 05/01/2024] Open
Abstract
Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.
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Affiliation(s)
- Wei Lan
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronic and Information, Guangxi University, No. 100 Daxue Road, Xixiangtang District, Nanning 530004, China
| | - Haibo Liao
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronic and Information, Guangxi University, No. 100 Daxue Road, Xixiangtang District, Nanning 530004, China
| | - Qingfeng Chen
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronic and Information, Guangxi University, No. 100 Daxue Road, Xixiangtang District, Nanning 530004, China
| | - Lingzhi Zhu
- School of Computer and Information Science, Hunan Institute of Technology, No. 18 Henghua Road, Zhuhui District, Hengyang 421002, China
| | - Yi Pan
- School of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Avenue, Shenzhen University Town, Nanshan District, Shenzhen 518055, China
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Plenty Rd, Bundoora, Melbourne, Victoria 3086, Australia
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Choi JM, Park C, Chae H. moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud. PeerJ 2024; 12:e17006. [PMID: 38426141 PMCID: PMC10903350 DOI: 10.7717/peerj.17006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.
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Affiliation(s)
- Joung Min Choi
- Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States
| | - Chaelin Park
- Division of Computer Science, Sookmyung Women’s University, Seoul, South Korea
| | - Heejoon Chae
- Division of Computer Science, Sookmyung Women’s University, Seoul, South Korea
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9
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Wang H, Han X, Ren J, Cheng H, Li H, Li Y, Li X. A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:736-764. [PMID: 38303441 DOI: 10.3934/mbe.2024031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.
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Affiliation(s)
- Huiqing Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Xiao Han
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Jianxue Ren
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Hao Cheng
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Haolin Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Ying Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Xue Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
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Choi SR, Lee M. Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review. BIOLOGY 2023; 12:1033. [PMID: 37508462 PMCID: PMC10376273 DOI: 10.3390/biology12071033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/18/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023]
Abstract
The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.
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Affiliation(s)
| | - Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea;
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Wekesa JS, Kimwele M. A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment. Front Genet 2023; 14:1199087. [PMID: 37547471 PMCID: PMC10398577 DOI: 10.3389/fgene.2023.1199087] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 07/11/2023] [Indexed: 08/08/2023] Open
Abstract
Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.
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Angione C, Wang H, Burtt N. Editorial: Artificial intelligence for data discovery and reuse in endocrinology and metabolism. Front Endocrinol (Lausanne) 2023; 14:1180254. [PMID: 37214239 PMCID: PMC10196622 DOI: 10.3389/fendo.2023.1180254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 04/18/2023] [Indexed: 05/24/2023] Open
Affiliation(s)
- Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, North Yorkshire, United Kingdom
- Centre for Digital Innovation, Teesside University, North Yorkshire, United Kingdom
- National Horizons Centre, Teesside University, North Yorkshire, United Kingdom
| | - Huajin Wang
- Open Science & Data Collaborations, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Noël Burtt
- Medical and Population Genetics Program and Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, United States
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