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Sienkiewicz K, Chen J, Chatrath A, Lawson JT, Sheffield NC, Zhang L, Ratan A. Detecting molecular subtypes from multi-omics datasets using SUMO. Cell Rep Methods 2022; 2:100152. [PMID: 35211690 PMCID: PMC8865426 DOI: 10.1016/j.crmeth.2021.100152] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 08/27/2021] [Accepted: 12/21/2021] [Indexed: 12/31/2022]
Abstract
We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for "IDH wild type, with molecular features of glioblastoma" by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.
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Affiliation(s)
- Karolina Sienkiewicz
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Jinyu Chen
- Department of Mathematics and Computational Biology Program, National University of Singapore, Singapore 119076, Singapore
| | - Ajay Chatrath
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - John T. Lawson
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Nathan C. Sheffield
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
- University of Virginia Cancer Center, Charlottesville, VA 22908, USA
| | - Louxin Zhang
- Department of Mathematics and Computational Biology Program, National University of Singapore, Singapore 119076, Singapore
| | - Aakrosh Ratan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
- University of Virginia Cancer Center, Charlottesville, VA 22908, USA
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Plante A, Weinstein H. Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol. Molecules 2021; 26:molecules26103059. [PMID: 34065494 PMCID: PMC8161244 DOI: 10.3390/molecules26103059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/11/2021] [Accepted: 05/11/2021] [Indexed: 01/14/2023] Open
Abstract
Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins, and the effects of ligand binding. The need for, and successes of, MD simulations in providing this type of essential information are well documented, but so are the challenges presented by the size of the resulting datasets encoding the desired information. The difficulty of extracting information on mechanistically important state-to-state transitions in response to ligand binding and other interactions is compounded by these being rare events in the MD trajectories of complex molecular machines, such as G-protein-coupled receptors (GPCRs). To address this problem, we have developed a protocol for the efficient detection of such events. We show that the novel Rare Event Detection (RED) protocol reveals functionally relevant and pharmacologically discriminating responses to the binding of different ligands to the 5-HT2AR orthosteric site in terms of clearly defined, structurally coherent, and temporally ordered conformational transitions. This information from the RED protocol offers new insights into specific ligand-determined functional mechanisms encoded in the MD trajectories, which opens a new and rigorously reproducible path to understanding drug activity with application in drug discovery.
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Affiliation(s)
- Ambrose Plante
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA;
| | - Harel Weinstein
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA;
- Institute for Computational Biomedicine, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA
- Correspondence: ; Tel.: +1-212-746-6358
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Dong A, Li Z, Zheng Q. Transferred Subspace Learning Based on Non-negative Matrix Factorization for EEG Signal Classification. Front Neurosci 2021; 15:647393. [PMID: 33841089 PMCID: PMC8024531 DOI: 10.3389/fnins.2021.647393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/22/2021] [Indexed: 11/13/2022] Open
Abstract
EEG signal classification has been a research hotspot recently. The combination of EEG signal classification with machine learning technology is very popular. Traditional machine leaning methods for EEG signal classification assume that the EEG signals are drawn from the same distribution. However, the assumption is not always satisfied with the practical applications. In practical applications, the training dataset and the testing dataset are from different but related domains. How to make best use of the training dataset knowledge to improve the testing dataset is critical for these circumstances. In this paper, a novel method combining the non-negative matrix factorization technology and the transfer learning (NMF-TL) is proposed for EEG signal classification. Specifically, the shared subspace is extracted from the testing dataset and training dataset using non-negative matrix factorization firstly and then the shared subspace and the original feature space are combined to obtain the final EEG signal classification results. On the one hand, the non-negative matrix factorization can assure to obtain essential information between the testing and the training dataset; on the other hand, the combination of shared subspace and the original feature space can fully use all the signals including the testing and the training dataset. Extensive experiments on Bonn EEG confirmed the effectiveness of the proposed method.
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Affiliation(s)
- Aimei Dong
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Science), Jinan, China
| | - Zhigang Li
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Science), Jinan, China
| | - Qiuyu Zheng
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Science), Jinan, China
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