Browsing by Subject "Network models"

Now showing 1 - 4 of 4
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    Author Correction: Spread of pathological tau proteins through communicating neurons in human Alzheimer’s disease
    (Springer Nature, 2021-08-05) Vogel, Jacob W.; Iturria-Medina, Yasser; Strandberg, Olof T.; Smith, Ruben; Levitis, Elizabeth; Evans, Alan C.; Hansson, Oskar; Alzheimer’s Disease Neuroimaging Initiative; Swedish BioFinder Study; Radiology and Imaging Sciences, School of Medicine
    Correction to: Nature Communications 10.1038/s41467-020-15701-2, published online 26 May 2020. The original version of the Supplementary information associated with this Article inadvertently omitted Supplementary Table S1. The HTML has been updated to include a corrected version of the Supplementary information.
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    Brain network hypersensitivity underlies pain crises in sickle cell disease
    (Springer Nature, 2024-03-27) Joo, Pangyu; Kim, Minkyung; Kish, Brianna; Nair, Vidhya Vijayakrishnan; Tong, Yunjie; Liu, Ziyue; O’Brien, Andrew R. W.; Harte, Steven E.; Harris, Richard E.; Lee, UnCheol; Wang, Ying; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Sickle cell disease (SCD) is a genetic disorder causing painful and unpredictable Vaso-occlusive crises (VOCs) through blood vessel blockages. In this study, we propose explosive synchronization (ES) as a novel approach to comprehend the hypersensitivity and occurrence of VOCs in the SCD brain network. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) as well as VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited lower alpha frequency than controls. SCD patients showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, stronger FDA was observed in SCD patients with a higher frequency of VOCs and EEG recording near VOC. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Our model demonstrated that a stronger FDA could be linked to increased sensitivity and frequency of VOCs. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.
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    Network Models for Capturing Molecular Feature and Predicting Drug Target for Various Cancers
    (2020-12) Liu, Enze; Liu, Xiaowen; Wu, Huanmei; Zhang, Chi; Wan, Jun; Cao, Sha; Liu, Lang
    Network-based modeling and analysis have been widely used for capturing molecular trajectories of cellular processes. For complex diseases like cancers, if we can utilize network models to capture adequate features, we can gain a better insight of the mechanism of cancers, which will further facilitate the identification of molecular vulnerabilities and the development targeted therapy. Based on this rationale, we conducted the following four studies: A novel algorithm ‘FFBN’ is developed for reconstructing directional regulatory networks (DEGs) from tissue expression data to identify molecular features. ‘FFBN’ shows unique capability of fast and accurately reconstructing genome-wide DEGs compared to existing methods. FFBN is further used to capture molecular features among liver metastasis, primary liver cancers and primary colon cancers. Comparisons among these features lead to new understandings of how liver metastasis is similar to its primary and distant cancers. ‘SCN’ is a novel algorithm that incorporates multiple types of omics data to reconstruct functional networks for not only revealing molecular vulnerabilities but also predicting drug targets on top of that. The molecular vulnerabilities are discovered via tissue-specific networks and drug targets are predicted via cell-line specific networks. SCN is tested on primary pancreatic cancers and the predictions coincide with current treatment plans. ‘SCN website’ is a web application of ‘SCN’ algorithm. It allows users to easily submit their own data and get predictions online. Meanwhile the predictions are displayed along with network graphs and survival curves. ‘DSCN’ is a novel algorithm derived from ‘SCN’. Instead of predicting single targets like ‘SCN’, ‘DSCN’ applies a novel approach for predicting target combinations using multiple omics data and network models. In conclusion, our studies revealed how genes regulate each other in the form of networks and how these networks can be used for unveiling cancer-related biological processes. Our algorithms and website facilitate capturing molecular features for cancers and predicting novel drug targets.
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    The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services
    (Springer Nature, 2019-05-23) Avesani, Paolo; McPherson, Brent; Hayashi, Soichi; Caiafa, Cesar F.; Henschel, Robert; Garyfallidis, Eleftherios; Kitchell, Lindsey; Bullock, Daniel; Patterson, Andrew; Olivetti, Emanuele; Sporns, Olaf; Saykin, Andrew J.; Wang, Lei; Dinov, Ivo; Hancock, David; Caron, Bradley; Qian, Yiming; Pestilli, Franco; Radiology and Imaging Sciences, School of Medicine
    We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.