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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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Zhang P, Zhang D, Zhou W, Wang L, Wang B, Zhang T, Li S. Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Brief Bioinform 2023; 25:bbad518. [PMID: 38197310 PMCID: PMC10777171 DOI: 10.1093/bib/bbad518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 11/03/2023] [Accepted: 11/30/2023] [Indexed: 01/11/2024] Open
Abstract
Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.
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Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Dingfan Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wuai Zhou
- China Mobile Information System Integration Co., Ltd, Beijing 100032, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Boyang Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tingyu Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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Jakabek D, Power BD, Spotorno N, Macfarlane MD, Walterfang M, Velakoulis D, Nilsson C, Waldö ML, Lätt J, Nilsson M, van Westen D, Lindberg O, Looi JCL, Santillo AF. Structural and microstructural thalamocortical network disruption in sporadic behavioural variant frontotemporal dementia. Neuroimage Clin 2023; 39:103471. [PMID: 37473493 PMCID: PMC10371821 DOI: 10.1016/j.nicl.2023.103471] [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] [Received: 02/06/2023] [Revised: 06/09/2023] [Accepted: 07/06/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Using multi-block methods we combined multimodal neuroimaging metrics of thalamic morphology, thalamic white matter tract diffusion metrics, and cortical thickness to examine changes in behavioural variant frontotemporal dementia. (bvFTD). METHOD Twenty-three patients with sporadic bvFTD and 24 healthy controls underwent structural and diffusion MRI scans. Clinical severity was assessed using the Clinical Dementia Rating scale and behavioural severity using the Frontal Behaviour Inventory by patient caregivers. Thalamic volumes were manually segmented. Anterior and posterior thalamic radiation fractional anisotropy and mean diffusivity were extracted using Tract-Based Spatial Statistics. Finally, cortical thickness was assessed using Freesurfer. We used shape analyses, diffusion measures, and cortical thickness as features in sparse multi-block partial least squares (PLS) discriminatory analyses to classify participants within bvFTD or healthy control groups. Sparsity was tuned with five-fold cross-validation repeated 10 times. Final model fit was assessed using permutation testing. Additionally, sparse multi-block PLS was used to examine associations between imaging features and measures of dementia severity. RESULTS Bilateral anterior-dorsal thalamic atrophy, reduction in mean diffusivity of thalamic projections, and frontotemporal cortical thinning, were the main features predicting bvFTD group membership. The model had a sensitivity of 96%, specificity of 68%, and was statistically significant using permutation testing (p = 0.012). For measures of dementia severity, we found similar involvement of regional thalamic and cortical areas as in discrimination analyses, although more extensive thalamo-cortical white matter metric changes. CONCLUSIONS Using multimodal neuroimaging, we demonstrate combined structural network dysfunction of anterior cortical regions, cortical-thalamic projections, and anterior thalamic regions in sporadic bvFTD.
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Affiliation(s)
| | - Brian D Power
- School of Medicine, The University of Notre Dame Australia, Fremantle, Australia
| | - Nicola Spotorno
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | | | - Mark Walterfang
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Dennis Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Christer Nilsson
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | - Maria Landqvist Waldö
- Clinical Sciences Helsingborg, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jimmy Lätt
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Markus Nilsson
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Danielle van Westen
- Imaging and Function, Skane University Hospital, Lund, Sweden; Diagnostic Radiology, Institution for Clinical Sciences, Lund University, Lund, Sweden
| | - Olof Lindberg
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | - Jeffrey C L Looi
- Academic Unit of Psychiatry and Addiction Medicine, The Australian National University School of Medicine and Psychology, Canberra Hospital, Canberra, Australian Capital Territory, Australia
| | - Alexander F Santillo
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden.
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Dennis EL, Keleher F, Tate DF, Wilde EA. The Role of Neuroimaging in Evolving TBI Research and Clinical Practice. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.24.23286258. [PMID: 36865222 PMCID: PMC9980266 DOI: 10.1101/2023.02.24.23286258] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
Abstract
Neuroimaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) have been widely adopted in the clinical diagnosis and management of traumatic brain injury (TBI), particularly at the more acute and severe levels of injury. Additionally, a number of advanced applications of MRI have been employed in TBI-related clinical research with great promise, and researchers have used these techniques to better understand underlying mechanisms, progression of secondary injury and tissue perturbation over time, and relation of focal and diffuse injury to later outcome. However, the acquisition and analysis time, the cost of these and other imaging modalities, and the need for specialized expertise have represented historical barriers in extending these tools in clinical practice. While group studies are important in detecting patterns, heterogeneity among patient presentation and limited sample sizes from which to compare individual level data to well-developed normative data have also played a role in the limited translatability of imaging to wider clinical application. Fortunately, the field of TBI has benefitted from increased public and scientific awareness of the prevalence and impact of TBI, particularly in head injury related to recent military conflicts and sport-related concussion. This awareness parallels an increase in federal funding in the United States and other countries allocated to investigation in these areas. In this article we summarize funding and publication trends since the mainstream adoption of imaging in TBI to elucidate evolving trends and priorities in the application of different techniques and patient populations. We also review recent and ongoing efforts to advance the field through promoting reproducibility, data sharing, big data analytic methods, and team science. Finally, we discuss international collaborative efforts to combine and harmonize neuroimaging, cognitive, and clinical data, both prospectively and retrospectively. Each of these represent unique, but related, efforts that facilitate closing gaps between the use of advanced imaging solely as a research tool and the use of it in clinical diagnosis, prognosis, and treatment planning and monitoring.
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Affiliation(s)
- Emily L Dennis
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT
| | - Finian Keleher
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT
| | - David F Tate
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT
| | - Elisabeth A Wilde
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT
- Baylor College of Medicine, Houston, TX
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Liu G, Ge H, Li T, Su S, Wang S. Multi-view subspace enhanced representation of manifold regularization and low-rank tensor constraint. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01729-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Hypergraph regularized low-rank tensor multi-view subspace clustering via L1 norm constraint. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04277-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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Tustison NJ, Altes TA, Qing K, He M, Miller GW, Avants BB, Shim YM, Gee JC, Mugler JP, Mata JF. Image- versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images. Magn Reson Med 2021; 86:2822-2836. [PMID: 34227163 DOI: 10.1002/mrm.28908] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/05/2021] [Accepted: 06/09/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE To characterize the differences between histogram-based and image-based algorithms for segmentation of hyperpolarized gas lung images. METHODS Four previously published histogram-based segmentation algorithms (ie, linear binning, hierarchical k-means, fuzzy spatial c-means, and a Gaussian mixture model with a Markov random field prior) and an image-based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation-based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision. RESULTS Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram-based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image-based convolutional neural network. CONCLUSIONS Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well-known Advanced Normalization Tools ecosystem.
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Affiliation(s)
- Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Talissa A Altes
- Department of Radiology, University of Missouri, Columbia, Missouri, USA
| | - Kun Qing
- Department of Radiation Oncology, City of Hope, Los Angeles, California, USA
| | - Mu He
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - G Wilson Miller
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Brian B Avants
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Yun M Shim
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John P Mugler
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Jaime F Mata
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
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Jia X, Jing XY, Zhu X, Cai Z, Hu CH. Co-embedding: a semi-supervised multi-view representation learning approach. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06599-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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