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Zhu S, Wang W, Fang W, Cui M. Autoencoder-assisted latent representation learning for survival prediction and multi-view clustering on multi-omics cancer subtyping. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21098-21119. [PMID: 38124589 DOI: 10.3934/mbe.2023933] [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: 12/23/2023]
Abstract
Cancer subtyping (or cancer subtypes identification) based on multi-omics data has played an important role in advancing diagnosis, prognosis and treatment, which triggers the development of advanced multi-view clustering algorithms. However, the high-dimension and heterogeneity of multi-omics data make great effects on the performance of these methods. In this paper, we propose to learn the informative latent representation based on autoencoder (AE) to naturally capture nonlinear omic features in lower dimensions, which is helpful for identifying the similarity of patients. Moreover, to take advantage of survival information or clinical information, a multi-omic survival analysis approach is embedded when integrating the similarity graph of heterogeneous data at the multi-omics level. Then, the clustering method is performed on the integrated similarity to generate subtype groups. In the experimental part, the effectiveness of the proposed framework is confirmed by evaluating five different multi-omics datasets, taken from The Cancer Genome Atlas. The results show that AE-assisted multi-omics clustering method can identify clinically significant cancer subtypes.
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Affiliation(s)
- Shuwei Zhu
- School of Artificial Intelligence and Computer Science, Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China
| | - Wenping Wang
- School of Artificial Intelligence and Computer Science, Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China
| | - Wei Fang
- School of Artificial Intelligence and Computer Science, Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China
| | - Meiji Cui
- School of Intelligent Manufacturing, Nanjing University of Science and Technology, Nanjing 210094, China
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2
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Manganaro L, Bianco S, Bironzo P, Cipollini F, Colombi D, Corà D, Corti G, Doronzo G, Errico L, Falco P, Gandolfi L, Guerrera F, Monica V, Novello S, Papotti M, Parab S, Pittaro A, Primo L, Righi L, Sabbatini G, Sandri A, Vattakunnel S, Bussolino F, Scagliotti GV. Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer. Sci Rep 2023; 13:7759. [PMID: 37173325 PMCID: PMC10182023 DOI: 10.1038/s41598-023-33954-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy.
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Affiliation(s)
- L Manganaro
- aizoOn Technology Consulting S.R.L, Torino, Italy
| | - S Bianco
- aizoOn Technology Consulting S.R.L, Torino, Italy
| | - P Bironzo
- Medical Oncology Division at San Luigi Hospital, Department of Oncology, University of Torino, Orbassano (TO), Italy
| | - F Cipollini
- aizoOn Technology Consulting S.R.L, Torino, Italy
| | - D Colombi
- aizoOn Technology Consulting S.R.L, Torino, Italy
| | - D Corà
- Department of Translational Medicine, Piemonte Orientale University, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Diseases-CAAD, Novara, Italy
| | - G Corti
- Department of Oncology, University of Torino, 10060, Candiolo, Italy
- Candiolo Cancer Institute-IRCCS-FPO, 10060, Candiolo, Italy
| | - G Doronzo
- Department of Oncology, University of Torino, 10060, Candiolo, Italy
- Candiolo Cancer Institute-IRCCS-FPO, 10060, Candiolo, Italy
| | - L Errico
- Division of Thoracic Surgery at AOU San Luigi, Department of Oncology, University of Torino, Orbassano (TO), Italy
| | - P Falco
- aizoOn Technology Consulting S.R.L, Torino, Italy
| | - L Gandolfi
- Department of Oncology, University of Torino, 10060, Candiolo, Italy
- Candiolo Cancer Institute-IRCCS-FPO, 10060, Candiolo, Italy
| | - F Guerrera
- Division of Thoracic Surgery at AOU Città della Salute e della Scienza, Department of Surgical Sciences, University of Torino, Torino, Italy
| | - V Monica
- Department of Oncology, University of Torino, 10060, Candiolo, Italy
- Candiolo Cancer Institute-IRCCS-FPO, 10060, Candiolo, Italy
| | - S Novello
- Medical Oncology Division at San Luigi Hospital, Department of Oncology, University of Torino, Orbassano (TO), Italy
| | - M Papotti
- Pathology Division at AOU Città della Salute e della Scienza, Department of Oncology, University of Torino, Torino, Italy
| | - S Parab
- Department of Oncology, University of Torino, 10060, Candiolo, Italy
- Candiolo Cancer Institute-IRCCS-FPO, 10060, Candiolo, Italy
| | - A Pittaro
- Pathology Division at AOU Città della Salute e della Scienza, Department of Oncology, University of Torino, Torino, Italy
| | - L Primo
- Department of Oncology, University of Torino, 10060, Candiolo, Italy
- Candiolo Cancer Institute-IRCCS-FPO, 10060, Candiolo, Italy
| | - L Righi
- Pathology Division at AOU San Luigi, Department of Oncology, University of Torino, Orbassano (TO), Italy
| | - G Sabbatini
- aizoOn Technology Consulting S.R.L, Torino, Italy
| | - A Sandri
- Division of Thoracic Surgery at AOU San Luigi, Department of Oncology, University of Torino, Orbassano (TO), Italy
| | | | - F Bussolino
- Department of Oncology, University of Torino, 10060, Candiolo, Italy
- Candiolo Cancer Institute-IRCCS-FPO, 10060, Candiolo, Italy
| | - G V Scagliotti
- Medical Oncology Division at San Luigi Hospital, Department of Oncology, University of Torino, Orbassano (TO), Italy.
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3
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Ge S, Liu J, Cheng Y, Meng X, Wang X. Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping. Brief Bioinform 2023; 24:6850565. [PMID: 36445207 DOI: 10.1093/bib/bbac500] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/19/2022] [Accepted: 10/22/2022] [Indexed: 11/30/2022] Open
Abstract
Driven by multi-omics data, some multi-view clustering algorithms have been successfully applied to cancer subtypes prediction, aiming to identify subtypes with biometric differences in the same cancer, thereby improving the clinical prognosis of patients and designing personalized treatment plan. Due to the fact that the number of patients in omics data is much smaller than the number of genes, multi-view spectral clustering based on similarity learning has been widely developed. However, these algorithms still suffer some problems, such as over-reliance on the quality of pre-defined similarity matrices for clustering results, inability to reasonably handle noise and redundant information in high-dimensional omics data, ignoring complementary information between omics data, etc. This paper proposes multi-view spectral clustering with latent representation learning (MSCLRL) method to alleviate the above problems. First, MSCLRL generates a corresponding low-dimensional latent representation for each omics data, which can effectively retain the unique information of each omics and improve the robustness and accuracy of the similarity matrix. Second, the obtained latent representations are assigned appropriate weights by MSCLRL, and global similarity learning is performed to generate an integrated similarity matrix. Third, the integrated similarity matrix is used to feed back and update the low-dimensional representation of each omics. Finally, the final integrated similarity matrix is used for clustering. In 10 benchmark multi-omics datasets and 2 separate cancer case studies, the experiments confirmed that the proposed method obtained statistically and biologically meaningful cancer subtypes.
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Affiliation(s)
- Shuguang Ge
- School of Information and Control Engineering, China University of Mining and Technology, No. 1, Daxue Road, 221116 Xuzhou, Jiangsu, China.,Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, No. 1, Daxue Road, 221116 Xuzhou, Jiangsu, China
| | - Jian Liu
- School of Information and Control Engineering, China University of Mining and Technology, No. 1, Daxue Road, 221116 Xuzhou, Jiangsu, China.,Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, No. 1, Daxue Road, 221116 Xuzhou, Jiangsu, China
| | - Yuhu Cheng
- School of Information and Control Engineering, China University of Mining and Technology, No. 1, Daxue Road, 221116 Xuzhou, Jiangsu, China.,Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, No. 1, Daxue Road, 221116 Xuzhou, Jiangsu, China
| | - Xiaojing Meng
- School of Medical Information and Engineering, Xuzhou Medical University, No. 209, Tongshan Road, 221116 Xuzhou, Jiangsu, China
| | - Xuesong Wang
- School of Information and Control Engineering, China University of Mining and Technology, No. 1, Daxue Road, 221116 Xuzhou, Jiangsu, China.,Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, No. 1, Daxue Road, 221116 Xuzhou, Jiangsu, China
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Mokou M, Narayanasamy S, Stroggilos R, Balaur IA, Vlahou A, Mischak H, Frantzi M. A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures. Methods Mol Biol 2023; 2684:59-99. [PMID: 37410228 DOI: 10.1007/978-1-0716-3291-8_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.
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Affiliation(s)
- Marika Mokou
- Department of Biomarker Research, Mosaiques Diagnostics, Hannover, Germany.
| | - Shaman Narayanasamy
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rafael Stroggilos
- Systems Biology Center, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Irina-Afrodita Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Antonia Vlahou
- Systems Biology Center, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Harald Mischak
- Department of Biomarker Research, Mosaiques Diagnostics, Hannover, Germany
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Maria Frantzi
- Department of Biomarker Research, Mosaiques Diagnostics, Hannover, Germany
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5
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Lin P, Zhang L, Zuo J. Data-driven prediction of building energy consumption using an adaptive multi-model fusion approach. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Liu C, Cao W, Wu S, Shen W, Jiang D, Yu Z, Wong HS. Supervised Graph Clustering for Cancer Subtyping Based on Survival Analysis and Integration of Multi-Omic Tumor Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1193-1202. [PMID: 32750893 DOI: 10.1109/tcbb.2020.3010509] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Identifying cancer subtypes by integration of multi-omic data is beneficial to improve the understanding of disease progression, and provides more precise treatment for patients. Cancer subtypes identification is usually accomplished by clustering patients with unsupervised learning approaches. Thus, most existing integrative cancer subtyping methods are performed in an entirely unsupervised way. An integrative cancer subtyping approach can be improved to discover clinically more relevant cancer subtypes when considering the clinical survival response variables. In this study, we propose a Survival Supervised Graph Clustering (S2GC)for cancer subtyping by taking into consideration survival information. Specifically, we use a graph to represent similarity of patients, and develop a multi-omic survival analysis embedding with patient-to-patient similarity graph learning for cancer subtype identification. The multi-view (omic)survival analysis model and graph of patients are jointly learned in a unified way. The learned optimal graph can be unitized to cluster cancer subtypes directly. In the proposed model, the survival analysis model and adaptive graph learning could positively reinforce each other. Consequently, the survival time can be considered as supervised information to improve the quality of the similarity graph and explore clinically more relevant subgroups of patients. Experiments on several representative multi-omic cancer datasets demonstrate that the proposed method achieves better results than a number of state-of-the-art methods. The results also suggest that our method is able to identify biologically meaningful subgroups for different cancer types. (Our Matlab source code is available online at github: https://github.com/CLiu272/S2GC).
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7
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Zhong L, Meng Q, Chen Y, Du L, Wu P. A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data. BMC Bioinformatics 2021; 22:475. [PMID: 34600466 PMCID: PMC8487515 DOI: 10.1186/s12859-021-04391-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 09/22/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. RESULTS In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. CONCLUSION The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.
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Affiliation(s)
- Lianxin Zhong
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Qingfang Meng
- School of Information Science and Engineering, University of Jinan, Jinan, China.
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Lei Du
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
| | - Peng Wu
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, 250022, China
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8
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Cancer Subtype Recognition Based on Laplacian Rank Constrained Multiview Clustering. Genes (Basel) 2021; 12:genes12040526. [PMID: 33916856 PMCID: PMC8065670 DOI: 10.3390/genes12040526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/28/2021] [Accepted: 03/31/2021] [Indexed: 12/13/2022] Open
Abstract
Integrating multigenomic data to recognize cancer subtype is an important task in bioinformatics. In recent years, some multiview clustering algorithms have been proposed and applied to identify cancer subtype. However, these clustering algorithms ignore that each data contributes differently to the clustering results during the fusion process, and they require additional clustering steps to generate the final labels. In this paper, a new one-step method for cancer subtype recognition based on graph learning framework is designed, called Laplacian Rank Constrained Multiview Clustering (LRCMC). LRCMC first forms a graph for a single biological data to reveal the relationship between data points and uses affinity matrix to encode the graph structure. Then, it adds weights to measure the contribution of each graph and finally merges these individual graphs into a consensus graph. In addition, LRCMC constructs the adaptive neighbors to adjust the similarity of sample points, and it uses the rank constraint on the Laplacian matrix to ensure that each graph structure has the same connected components. Experiments on several benchmark datasets and The Cancer Genome Atlas (TCGA) datasets have demonstrated the effectiveness of the proposed algorithm comparing to the state-of-the-art methods.
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A Hierarchical Clustering algorithm based on Silhouette Index for cancer subtype discovery from genomic data. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04636-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Xu J, Wu P, Chen Y, Meng Q, Dawood H, Dawood H. A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data. BMC Bioinformatics 2019; 20:527. [PMID: 31660856 PMCID: PMC6819613 DOI: 10.1186/s12859-019-3116-7] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 09/27/2019] [Indexed: 12/11/2022] Open
Abstract
Background 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. Results A new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods. Conclusion The new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes.
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Affiliation(s)
- Jing Xu
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
| | - Peng Wu
- School of Information Science and Engineering, University of Jinan, Jinan, China. .,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
| | - Qingfang Meng
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
| | - Hussain Dawood
- Department of Computer and Network Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Hassan Dawood
- Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan
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Jiang J, Xing F, Zeng X, Zou Q. Investigating Maize Yield-Related Genes in Multiple Omics Interaction Network Data. IEEE Trans Nanobioscience 2019; 19:142-151. [PMID: 31170079 DOI: 10.1109/tnb.2019.2920419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Zea mays (maize) is the highest yielding food crop globally, feeding large numbers of people across the planet. It is thus especially important to explore the key genes that affect maize production with prior knowledge. Merging multiple datasets of different types can improve the accuracy of candidate genes prediction results, so we constructed interaction networks using gene, mRNA, protein, and expression profile datasets. A network propagation schedule was used considering combined scores obtained by integrating both network scores and significance scores for each candidate gene based on the guilt-by-association principle. An SVM model was used to optimize the weighted parameters to achieve more reliable results, according to the accuracy of label classification. We found that integrating multiple omics data with more data types improves the reliability of the results. We investigated the GO terms particularly associated with the top 100 candidate genes and the known genes, and analyzed the roles that these genes play in determining the phenotype of maize. We hope that the candidate genes identified here will provide a biological perspective and contribute to maize breeding research.
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