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Published in MICCAI-MLMI, 2020
We proposed a simplex regression model with graph-constrained Elastic Net (GraphNet) to estimate functional networks enriched by structural connectivity in a biologically meaningful way with a low model complexity.
Citation: Mansu Kim, Jingxaun Bao, Kefei Liu, Bo-yong Park, Hyunjin Park, Li Shen. (2020). "Structural Connectivity Enriched Functional Brain network using Simplex Regression with GraphNet." International Conference on Medical Image Computing & Computer Assisted Intervention - Machine Learning in Medical Imaging Workshop.
Published in The 20th International Conference on BioInformatics and BioEngineering (ieeeBIBE2020), 2020
We applied four categories of machine learning strategies including nine different methods with two different feature representations to estimate the probability and severity of dental hard-tissue conditions from photographic tooth images.
Citation: Jingxuan Bao, Mansu Kim, Qing Sun, Anderson T. Hara, Gerardo Maupome, Li Shen. (2020). "Estimating Hard-tissue Conditions from Dental Images via Machine Learning." 20th International Conference on BioInformatics and BioEngineering (ieeeBIBE).
Published in Medical Image Analysis, 2020
This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging.
Citation: Mansu Kim, Jingxaun Bao, Kefei Liu, Bo-yong Park, Hyunjin Park, Jae Young Baik, Li Shen. (2020). "A Structural Enriched Functional Network: An Application to Predict Brain Cognitive Performance." Medical Image Analysis.
Published in Organization for Human Brain Mapping, 2021
Mining brain-wide gene expression data to identify imaging genomic modules via biclustering.
Citation: Jingxuan Bao, Mansu Kim, Xiaohui Yao, Trang Le, Patryk Orzechowski, Jingwen Yan, Andrew Saykin, Jason Moore, Li Shen. (2021). "Mining brain-wide gene expression data to identify imaging genomic modules via biclustering." Organization for Human Brain Mapping 2021.
Published in Organization for Human Brain Mapping, 2021
Identifying tissue specific transcriptomic effects on brain volume measures from GWAS summary data.
Citation: Hung Mai, Jingxuan Bao, Paul Thompson, Dokyoon Kim, Li Shen (2021). "Identifying tissue specific transcriptomic effects on brain volume measures from GWAS summary data." Organization for Human Brain Mapping 2021.
Published in MICCAI, 2021
We proposed a new semi-parametric Bayesian heritability estimation model to construct highly heritable imaging QTs.
Citation: Yize Zhao, Xiwen Zhao, Mansu Kim, Jingxuan Bao, Li Shen. (2021). "A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits." International Conference on Medical Image Computing & Computer Assisted Intervention.
Published in PSB, 2022
We propose a novel framework to identify the SNP-ROI association via regional morphometricity estimation of each studied single nucleotide polymorphism (SNP).
Citation: Jingxuan Bao, Zixuan Wen, Mansu Kim, Andrew J. Saykin, Paul M. Thompson, Yize Zhao, Li Shen, and for the Alzheimer’s Disease Neuroimaging Initiative. (2022). "Identifying imaging genetic associations via regional morphometricity estimation." Pacific Symposium on Biocomputing.
Published in PSB, 2022
We propose a novel method to define highly heritable brain regions.
Citation: Jingxuan Bao, Zixuan Wen, Mansu Kim, Brain N. Lee, Sang-Hyuk Jung, Christos Davatzikos, Andrew J. Saykin, Paul M. Thompson, Dokyoon Kim, Yize Zhao, Li Shen, and for the Alzheimer’s Disease Neuroimaging Initiative. (2021). "Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data." Pacific Symposium on Biocomputing.
Published in JAMA Psychiatry, 2022
Is late-life depression (LLD) associated with structural neuroimaging patterns?
Citation: Junhao Wen, Cynthia H. Y. Fu, Duygu Tosun, et al. (2022). "Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression." JAMA Psychiatry.
Published in Preprint, 2022
Mega-analysis of brain structural covariance, genetics, and clinical phenotypes
Citation: Junhao Wen, Ilya Nasrallah, Ahmed Abdulkadir, et al. (2022). "Mega-analysis of brain structural covariance, genetics, and clinical phenotypes." Preprint.
Published in BMC Medical Genomics, 2022
We proposed a novel disease-related brain transcriptomic mapping method to identify genes whose expression profiles spatially correlated with regional diagnostic effects on a studied brain trait.
Citation: Baik, J.Y., Kim, M., Bao, J., et al. (2022). "Identifying Alzheimer’s genes via brain transcriptome mapping." BMC Med Genomics.
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We applied two ways, impression based approach and demographic approach, in evaluation of the advertisements orders for a specific company with modified data provided by Clypd.
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We evaluated the appropriateness of the chest compression (CC) depth recommended in the current CPR guidelines and to characterize the optimal CC depth for an adult by body mass index (BMI).
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We are aiming to analyse the RNA methylation sites in a given set of genes in which the two main tasks are RNA methylation site detection and differential methylation analysis.
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We applied four categories of machine learning strategies including nine different methods with two different feature representations to estimate the probability and severity of dental hard-tissue conditions from photographic tooth images.
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We investigated the maximal size of submatrices with average of the values of such submatrices more than a fixed positive number in the Gaussian random matrix.
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We applied four categories of machine learning strategies including nine different methods with two different feature representations to estimate the probability and severity of dental hard-tissue conditions from photographic tooth images.
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We provide a Bayesian Mixture Model to classify the Case Fatality Rate (CFR) of Coronavirus Disease 2019 (COVID-19) in terms of region.
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We are developing a generalized Bayesian factor analysis (GBFA) framework, which is a sparse Bayesian factor analysis that can take into account the graph information.
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We are developing biomedical meaningful imaging-genetics biclustering algorithm.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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