Google
Google Scholar
Semantic Scholar
MS Academic
CiteSeerX
ORCIDYu-Ping Wang 0002
Person information
- affiliation: Tulane University, Department of Biomedical Engineering, New Orleans, LA, USA
Other persons with the same name
- Yuping Wang (aka: Yu-Ping Wang) — disambiguation page
- Yu-Ping Wang 0001
![[0000-0003-4129-7704]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0003-4129-7704")
(aka: Yuping Wang 0001) — Tsinghua University, Department of Computer Science and Technology, Beijing, China - Yuping Wang 0003 (aka: Yu-Ping Wang 0003) — Xidian University, School of Computer Science and Technology, Xi'an, China (and 1 more)
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2021
[j64]Julia M. Stephen, I. Solis, J. Janowich, M. Stern, Michaela R. Frenzel, Jacob A. Eastman, Mackenzie S. Mills, Christine M. Embury, N. M. Coolidge, Elizabeth Heinrichs-Graham, Andy R. Mayer, J. Liu, Yu-Ping Wang, Tony W. Wilson, Vince D. Calhoun:
The Developmental Chronnecto-Genomics (Dev-CoG) study: A multimodal study on the developing brain. NeuroImage 225: 117438 (2021)- 2020
- [j63]Gang Li, Chao Wang, Depeng Han, Yi-Pu Zhang, Peng Peng, Vince D. Calhoun, Yu-Ping Wang:
Deep Principal Correlated Auto-Encoders With Application to Imaging and Genomics Data Integration. IEEE Access 8: 20093-20107 (2020) - [j62]Oktay Agcaoglu, Tony W. Wilson, Yu-Ping Wang, Julia M. Stephen, Vince D. Calhoun:
Dynamic Resting-State Connectivity Differences in Eyes Open Versus Eyes Closed Conditions. Brain Connect. 10(9): 504-519 (2020) - [j61]Gang Li, Depeng Han, Chao Wang, Wenxing Hu, Vince D. Calhoun, Yu-Ping Wang:
Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia. Comput. Methods Programs Biomed. 183 (2020) - [j60]Chen Qiao, Yan Shi, Yu-Xian Diao, Vince D. Calhoun, Yu-Ping Wang:
Log-sum enhanced sparse deep neural network. Neurocomputing 407: 206-220 (2020) - [j59]Liangliang Liu, Jianhong Cheng, Quan Quan, Fang-Xiang Wu, Yu-Ping Wang, Jianxin Wang:
A survey on U-shaped networks in medical image segmentations. Neurocomputing 409: 244-258 (2020) - [j58]Zhongxing Zhou, Biao Cai, Gemeng Zhang, Aiying Zhang, Vince D. Calhoun, Yu-Ping Wang:
Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI. NeuroImage 221: 117190 (2020) - [j57]Abraham D. Killanin, Alex I. Wiesman, Elizabeth Heinrichs-Graham, Boman R. Groff, Michaela R. Frenzel, Jacob A. Eastman, Yu-Ping Wang, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson:
Development and sex modulate visuospatial oscillatory dynamics in typically-developing children and adolescents. NeuroImage 221: 117192 (2020) - [j56]Li Xiao, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms. IEEE Trans. Biomed. Eng. 67(3): 796-806 (2020) - [j55]Yuntong Bai, Pascal Zille, Wenxing Hu, Vince D. Calhoun, Yu-Ping Wang:
Biomarker Identification Through Integrating fMRI and Epigenetics. IEEE Trans. Biomed. Eng. 67(4): 1186-1196 (2020) - [j54]Min Wang, Ting-Zhu Huang, Jian Fang, Vince D. Calhoun, Yu-Ping Wang:
Integration of Imaging (epi)Genomics Data for the Study of Schizophrenia Using Group Sparse Joint Nonnegative Matrix Factorization. IEEE ACM Trans. Comput. Biol. Bioinform. 17(5): 1671-1681 (2020) - [j53]Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang:
Multimodal Sparse Classifier for Adolescent Brain Age Prediction. IEEE J. Biomed. Health Informatics 24(2): 336-344 (2020) - [j52]Yipu Zhang, Peng Peng, Yongfeng Ju, Gang Li, Vince D. Calhoun, Yu-Ping Wang:
Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity. IEEE J. Biomed. Health Informatics 24(9): 2621-2629 (2020) - [j51]Liangliang Liu, Fang-Xiang Wu, Yu-Ping Wang, Jianxin Wang:
Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images. IEEE J. Biomed. Health Informatics 24(11): 3215-3225 (2020) - [j50]Aiying Zhang, Biao Cai, Wenxing Hu, Bochao Jia, Faming Liang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang:
Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence. IEEE Trans. Medical Imaging 39(2): 357-365 (2020) - [j49]Gemeng Zhang, Biao Cai, Aiying Zhang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Estimating Dynamic Functional Brain Connectivity With a Sparse Hidden Markov Model. IEEE Trans. Medical Imaging 39(2): 488-498 (2020) - [j48]Li-Dan Kuang, Qiu-Hua Lin, Xiao-Feng Gong, Fengyu Cong, Yu-Ping Wang, Vince D. Calhoun:
Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint. IEEE Trans. Medical Imaging 39(4): 844-853 (2020) - [j47]Li Xiao, Junqi Wang, Peyman Hosseinzajeh Kassani, Yipu Zhang, Yuntong Bai, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data. IEEE Trans. Medical Imaging 39(5): 1746-1758 (2020) - [j46]Yuntong Bai, Pascal Zille, Vince D. Calhoun, Yu-Ping Wang:
Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data. IEEE Trans. Medical Imaging 39(6): 1801-1811 (2020) - [j45]Peyman Hosseinzadeh Kassani, Li Xiao, Gemeng Zhang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis. IEEE Trans. Medical Imaging 39(11): 3290-3299 (2020) - [c45]Gang Qu, Wenxing Hu, Li Xiao, Yu-Ping Wang:
A graph deep learning model for the classification of groups with different IQ using resting state fMRI. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2020: 113170A - [c44]Gemeng Zhang, Aiying Zhang, Vince D. Calhoun, Yu-Ping Wang:
A causal brain network estimation method leveraging Bayesian analysis and the PC algorithm. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2020: 113170X - [c43]Junqi Wang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Graph Laplacian learning based Fourier Transform for brain network analysis with resting state fMRI. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2020: 113171G - [c42]Peyman Hosseinzadeh Kassani, Vince D. Calhoun, Yu-Ping Wang:
Reduced sine hyperbolic polynomial model for brain neuro-developmental analysis. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2020: 1131706 - [c41]Li Xiao, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
A hypergraph learning method for brain functional connectivity network construction from fMRI data. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2020: 1131710 - [c40]Yuntong Bai, Vince D. Calhoun, Yu-Ping Wang:
Integration of multi-task fMRI for cognitive study by structure-enforced collaborative regression. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2020: 1131722 - [c39]Biao Cai, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity. Medical Imaging: Image Processing 2020: 113132F - [i7]Peyman Hosseinzadeh Kassani, Li Xiao, Gemeng Zhang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Causality based Feature Fusion for Brain Neuro-Developmental Analysis. CoRR abs/2001.08173 (2020) - [i6]Li Xiao, Xiang-Gen Xia, Yu-Ping Wang:
Exact and Robust Reconstruction of Integer Vectors Based on Multidimensional Chinese Remainder Theorem (MD-CRT). CoRR abs/2002.00087 (2020) - [i5]Wenxing Hu, Xianghe Meng, Yuntong Bai, Aiying Zhang, Biao Cai, Gemeng Zhang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang:
Interpretable multimodal fusion networks reveal mechanisms of brain cognition. CoRR abs/2006.09454 (2020) - [i4]Aiying Zhang, Gemeng Zhang, Biao Cai, Wenxing Hu, Li Xiao, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang:
Causal inference of brain connectivity from fMRI with ψ-Learning Incorporated Linear non-Gaussian Acyclic Model (ψ-LiNGAM). CoRR abs/2006.09536 (2020) - [i3]Aiying Zhang, Gemeng Zhang, Biao Cai, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang:
A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence. CoRR abs/2006.12618 (2020)
2010 – 2019
- 2019
- [j44]Junbo Duan, Charles Soussen, David Brie, Jérôme Idier, Yu-Ping Wang, Mingxi Wan:
A Parallelizable Framework for Segmenting Piecewise Signals. IEEE Access 7: 13217-13229 (2019) - [j43]Ashkan Faghiri, Julia M. Stephen, Yu-Ping Wang, Tony W. Wilson, Vince D. Calhoun:
Brain Development Includes Linear and Multiple Nonlinear Trajectories: A Cross-Sectional Resting-State Functional Magnetic Resonance Imaging Study. Brain Connect. 9(10): 777-788 (2019) - [j42]Md. Ashad Alam, Osamu Komori, Hong-Wen Deng, Vince D. Calhoun, Yu-Ping Wang:
Robust kernel canonical correlation analysis to detect gene-gene co-associations: A case study in genetics. J. Bioinform. Comput. Biol. 17(4): 1950028 (2019) - [j41]Michael P. Trevarrow, Max J. Kurz, Timothy J. McDermott, Alex I. Wiesman, Mackenzie S. Mills, Yu-Ping Wang, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson:
The developmental trajectory of sensorimotor cortical oscillations. NeuroImage 184: 455-461 (2019) - [j40]Christine M. Embury, Alex I. Wiesman, Amy L. Proskovec, Mackenzie S. Mills, Elizabeth Heinrichs-Graham, Yu-Ping Wang, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson:
Neural dynamics of verbal working memory processing in children and adolescents. NeuroImage 185: 191-197 (2019) - [j39]Junbo Duan, Jérôme Idier, Yu-Ping Wang, Mingxi Wan:
A Joint Least Squares and Least Absolute Deviation Model. IEEE Signal Process. Lett. 26(4): 543-547 (2019) - [j38]Biao Cai, Gemeng Zhang, Aiying Zhang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Capturing Dynamic Connectivity From Resting State fMRI Using Time-Varying Graphical Lasso. IEEE Trans. Biomed. Eng. 66(7): 1852-1862 (2019) - [j37]Li Xiao, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction. IEEE Trans. Biomed. Eng. 66(8): 2140-2151 (2019) - [j36]Wenxing Hu, Biao Cai, Aiying Zhang, Vince D. Calhoun, Yu-Ping Wang:
Deep Collaborative Learning With Application to the Study of Multimodal Brain Development. IEEE Trans. Biomed. Eng. 66(12): 3346-3359 (2019) - [j35]Aiying Zhang, Jian Fang, Faming Liang, Vince D. Calhoun, Yu-Ping Wang:
Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model. IEEE J. Biomed. Health Informatics 23(4): 1479-1489 (2019) - [c38]Ashkan Faghiri, Julia M. Stephen, Yu-Ping Wang, Tony W. Wilson, Vince D. Calhoun:
Using Gradient as a New Metric for Dynamic Connectivity Estimation from Resting fMRI Data. ISBI 2019: 1805-1808 - [c37]Yuntong Bai, Pascal Zille, Vince D. Calhoun, Yu-Ping Wang:
Extraction of co-expressed discriminative features of Schizophrenia in imaging epigenetics framework. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2019: 109530X - [c36]Biao Cai, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Improved estimation of dynamic connectivity from resting-state fMRI data. Medical Imaging: Image Processing 2019: 109490P - [i2]Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang:
Multimodal Sparse Classifier for Adolescent Brain Age Prediction. CoRR abs/1904.01070 (2019) - 2018
- [j34]Chao Xu, Jian Fang, Hui Shen, Yu-Ping Wang, Hong-Wen Deng:
EPS-LASSO: test for high-dimensional regression under extreme phenotype sampling of continuous traits. Bioinform. 34(12): 1996-2003 (2018) - [j33]Md. Ashad Alam, Vince D. Calhoun, Yu-Ping Wang:
Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics. Comput. Stat. Data Anal. 125: 70-85 (2018) - [j32]Wenxing Hu, Dongdong Lin, Shaolong Cao, Jingyu Liu, Jiayu Chen, Vince D. Calhoun, Yu-Ping Wang:
Adaptive Sparse Multiple Canonical Correlation Analysis With Application to Imaging (Epi)Genomics Study of Schizophrenia. IEEE Trans. Biomed. Eng. 65(2): 390-399 (2018) - [j31]Alexej Gossmann, Shaolong Cao, Damian Brzyski, Lan-Juan Zhao, Hong-Wen Deng, Yu-Ping Wang:
A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate Control. IEEE ACM Trans. Comput. Biol. Bioinform. 15(4): 1066-1078 (2018) - [j30]Su-Ping Deng, Wenxing Hu, Vince D. Calhoun, Yu-Ping Wang:
Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease. IEEE ACM Trans. Comput. Biol. Bioinform. 15(5): 1480-1491 (2018) - [j29]Jian Fang, Ji-Gang Zhang, Hong-Wen Deng, Yu-Ping Wang:
Joint Detection of Associations Between DNA Methylation and Gene Expression From Multiple Cancers. IEEE J. Biomed. Health Informatics 22(6): 1960-1969 (2018) - [j28]Jian Fang, Chao Xu, Pascal Zille, Dongdong Lin, Hong-Wen Deng, Vince D. Calhoun, Yu-Ping Wang:
Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation. IEEE Trans. Medical Imaging 37(4): 860-870 (2018) - [j27]Biao Cai, Pascal Zille, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI. IEEE Trans. Medical Imaging 37(5): 1224-1234 (2018) - [j26]Alexej Gossmann, Pascal Zille, Vince D. Calhoun, Yu-Ping Wang:
FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics. IEEE Trans. Medical Imaging 37(8): 1761-1774 (2018) - [j25]Pascal Zille, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang:
Fused Estimation of Sparse Connectivity Patterns From Rest fMRI - Application to Comparison of Children and Adult Brains. IEEE Trans. Medical Imaging 37(10): 2165-2175 (2018) - [j24]Pascal Zille, Vince D. Calhoun, Yu-Ping Wang:
Enforcing Co-Expression Within a Brain-Imaging Genomics Regression Framework. IEEE Trans. Medical Imaging 37(12): 2561-2571 (2018) - [c35]Jian Fang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang:
Detection of differentially developed functional connectivity patterns in adolescents based on tensor discriminative analysis. ISBI 2018: 10-14 - [c34]Junqi Wang, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang:
Integration of network topological features and graph Fourier transform for fMRI data analysis. ISBI 2018: 92-96 - [c33]Aiying Zhang, Jian Fang, Vince D. Calhoun, Yu-Ping Wang:
High dimensional latent Gaussian copula model for mixed data in imaging genetics. ISBI 2018: 105-109 - [c32]Wenxing Hu, Biao Cai, Vince D. Calhoun, Yu-Ping Wang:
Multi-modal Brain Connectivity Study Using Deep Collaborative Learning. GRAIL/Beyond-MIC@MICCAI 2018: 66-73 - 2017
- [j23]Hao He, Dongdong Lin, Ji-Gang Zhang, Yu-Ping Wang, Hong-Wen Deng:
Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network. BMC Bioinform. 18(1): 149:1-149:6 (2017) - [c31]Min Wang, Ting-Zhu Huang, Vince D. Calhoun, Jian Fang, Yu-Ping Wang:
Integration of multiple genomic imaging data for the study of schizophrenia using joint nonnegative matrix factorization. ICASSP 2017: 1083-1087 - [c30]Pascal Zille, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang:
Fused estimation of sparse connectivity patterns from rest fMRI. ICASSP 2017: 6160-6164 - [c29]Pascal Zille, Yu-Ping Wang:
Coupled Dimensionality-Reduction Model for Imaging Genomics. GRAIL/MFCA/MICGen@MICCAI 2017: 241-248 - [c28]Pascal Zille, Vince D. Calhoun, Yu-Ping Wang:
Enforcing Co-expression in Multimodal Regression Framework. PSB 2017: 96-107 - 2016
- [j22]Shaolong Cao, Huaizhen Qin, Alexej Gossmann, Hong-Wen Deng, Yu-Ping Wang:
Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations. Bioinform. 32(3): 330-337 (2016) - [j21]Jian Fang, Dongdong Lin, S. Charles Schulz, Zongben Xu, Vince D. Calhoun, Yu-Ping Wang:
Joint sparse canonical correlation analysis for detecting differential imaging genetics modules. Bioinform. 32(22): 3480-3488 (2016) - [j20]Dongdong Lin, Ji-Gang Zhang, Jingyao Li, Chao Xu, Hong-Wen Deng, Yu-Ping Wang:
An integrative imputation method based on multi-omics datasets. BMC Bioinform. 17: 247 (2016) - [j19]Chen Qiao, Wenfeng Jing, Jian Fang, Yu-Ping Wang:
The general critical analysis for continuous-time UPPAM recurrent neural networks. Neurocomputing 175: 40-46 (2016) - [j18]Junbo Duan, Charles Soussen, David Brie, Jérôme Idier, Mingxi Wan, Yu-Ping Wang:
Generalized LASSO with under-determined regularization matrices. Signal Process. 127: 239-246 (2016) - [j17]Junbo Duan, Mingxi Wan, Hong-Wen Deng, Yu-Ping Wang:
A Sparse Model Based Detection of Copy Number Variations From Exome Sequencing Data. IEEE Trans. Biomed. Eng. 63(3): 496-505 (2016) - [c27]Md. Ashad Alam, Vince D. Calhoun, Yu-Ping Wang:
Influence Function of Multiple Kernel Canonical Analysis to Identify Outliers in Imaging Genetics Data. BCB 2016: 210-219 - [c26]Md. Ashad Alam, Osamu Komori, Vince D. Calhoun, Yu-Ping Wang:
Robust Kernel Canonical Correlation Analysis to Detect Gene-Gene Interaction for Imaging Genetics Data. BCB 2016: 279-288 - [c25]Owen Richfield, Md. Ashad Alam, Vince D. Calhoun, Yu-Ping Wang:
Learning schizophrenia imaging genetics data via Multiple Kernel Canonical Correlation Analysis. BIBM 2016: 507-511 - [c24]Su-Ping Deng, Dongdong Lin, Vince D. Calhoun, Yu-Ping Wang:
Diagnosing schizophrenia by integrating genomic and imaging data through network fusion. BIBM 2016: 1307-1313 - [c23]Su-Ping Deng, Dongdong Lin, Vince D. Calhoun, Yu-Ping Wang:
Schizophrenia genes discovery by mining the minimum spanning trees from multi-dimensional imaging genomic data integration. BIBM 2016: 1493-1500 - [c22]Su-Ping Deng, Dongdong Lin, Vince D. Calhoun, Yu-Ping Wang:
Predicting schizophrenia by fusing networks from SNPs, DNA methylation and fMRI data. EMBC 2016: 1447-1450 - [c21]Wenxing Hu, Dongdong Lin, Vince D. Calhoun, Yu-Ping Wang:
Integration of SNPs-FMRI-methylation data with sparse multi-CCA for schizophrenia study. EMBC 2016: 3310-3313 - 2015
- [j16]Wenlong Tang, Chao Xu, Yu-Ping Wang, Hong-Wen Deng, Ji-Gang Zhang:
MicroRNA-mRNA interaction analysis to detect potential dysregulation in complex diseases. Netw. Model. Anal. Health Informatics Bioinform. 4(1) (2015) - [c20]Shaolong Cao, Huaizhen Qin, Alexej Gossmann, Hong-Wen Deng, Yu-Ping Wang:
Unified tests for fine scale mapping and identifying sparse high-dimensional sequence associations. BCB 2015: 241-249 - [c19]Jingyao Li, Dongdong Lin, Yu-Ping Wang:
Segmentation of Multicolor Fluorescence In-Situ Hybridization (M-FISH) image using an improved Fuzzy C-means clustering algorithm while incorporating both spatial and spectral information. BIBM 2015: 413-416 - [c18]Chen Qiao, Dongdong Lin, Shaolong Cao, Yu-Ping Wang:
The effective diagnosis of schizophrenia by using multi-layer RBMs deep networks. BIBM 2015: 603-606 - [c17]Junbo Duan, Charles Soussen, David Brie, Jérôme Idier, Yu-Ping Wang, Mingxi Wan:
An optimal method to segment piecewise poisson distributed signals with application to sequencing data. EMBC 2015: 6465-6468 - [c16]Dongdong Lin, Jingyao Li, Vince D. Calhoun, Yu-Ping Wang:
Detection of genetic factors associated with multiple correlated imaging phenotypes by a sparse regression model. ISBI 2015: 1368-1371 - 2014
- [j15]Lei Zhang, Yu-Fang Pei, Xiaoying Fu, Yong Lin, Yu-Ping Wang, Hong-Wen Deng:
FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model. Bioinform. 30(13): 1876-1883 (2014) - [j14]Lei Zhang, Yu-Fang Pei, Xiaoying Fu, Yong Lin, Yu-Ping Wang, Hong-Wen Deng:
FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model. Bioinform. 30(21): 3142 (2014) - [j13]Junbo Duan, Ji-Gang Zhang, Mingxi Wan, Hong-Wen Deng, Yu-Ping Wang:
Population clustering based on copy number variations detected from next generation sequencing data. J. Bioinform. Comput. Biol. 12(4) (2014) - [j12]Dongdong Lin, Vince D. Calhoun, Yu-Ping Wang:
Correspondence between fMRI and SNP data by group sparse canonical correlation analysis. Medical Image Anal. 18(6): 891-902 (2014) - [j11]Hongbao Cao, Junbo Duan, Dongdong Lin, Yin Yao Shugart, Vince D. Calhoun, Yu-Ping Wang:
Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs. NeuroImage 102: 220-228 (2014) - [j10]Junbo Duan, Hong-Wen Deng, Yu-Ping Wang:
Common Copy Number Variation Detection From Multiple Sequenced Samples. IEEE Trans. Biomed. Eng. 61(3): 928-937 (2014) - [c15]Shaolong Cao, Huaizhen Qin, Jian Li, Hong-Wen Deng, Yu-Ping Wang:
Scaled sparse high-dimensional tests for localizing sequence variants. BCB 2014: 79-87 - 2013
- [j9]Junbo Duan, Ji-Gang Zhang, Hong-Wen Deng, Yu-Ping Wang:
CNV-TV: A robust method to discover copy number variation from short sequencing reads. BMC Bioinform. 14: 150 (2013) - [j8]Dongdong Lin, Ji-Gang Zhang, Jingyao Li, Vince D. Calhoun, Hong-Wen Deng, Yu-Ping Wang:
Group sparse canonical correlation analysis for genomic data integration. BMC Bioinform. 14: 245 (2013) - [j7]Jingyao Li, Dongdong Lin, Hongbao Cao, Yu-Ping Wang:
An improved sparse representation model with structural information for Multicolour Fluorescence In-Situ Hybridization (M-FISH) image classification. BMC Syst. Biol. 7(S-4): S5 (2013) - [j6]Wenlong Tang, Junbo Duan, Ji-Gang Zhang, Yu-Ping Wang:
Subtyping glioblastoma by combining miRNA and mRNA expression data using compressed sensing-based approach. EURASIP J. Bioinform. Syst. Biol. 2013: 2 (2013) - [c14]Shaolong Cao, Huaizhen Qin, Hong-Wen Deng, Yu-Ping Wang:
A generalized sparse regression model with adjustment of pedigree structure for variant detection from next generation sequencing data. BCB 2013: 191 - [c13]Junbo Duan, Mingxi Wan, Hong-Wen Deng, Yu-Ping Wang:
Modeling exome sequencing data with generalized Gaussian distribution with application to copy number variation detection. BIBM 2013: 1-7 - [c12]Dongdong Lin, Hao He, Jingyao Li, Hong-Wen Deng, Vince D. Calhoun, Yu-Ping Wang:
Network-based investigation of genetic modules associated with functional brain networks in schizophrenia. BIBM 2013: 9-16 - [c11]Dongdong Lin, Ji-Gang Zhang, Jingyao Li, Vince D. Calhoun, Yu-Ping Wang:
Identifying genetic connections with brain functions in schizophrenia using group sparse canonical correlation analysis. ISBI 2013: 278-281 - [c10]Hongbao Cao, Junbo Duan, Dongdong Lin, Vincent D. Calhoun, Yu-Ping Wang:
Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNP data. ISBI 2013: 756-759 - [c9]Jingyao Li, Dongdong Lin, Yu-Ping Wang:
Classification of multicolor fluorescence in-situ hybridization (M-FISH) image using structure based sparse representation model with different constrains. ISBI 2013: 1352-1355 - 2012
- [j5]Hongbao Cao, Junbo Duan, Dongdong Lin, Yu-Ping Wang:
Sparse Representation Based Clustering for Integrated Analysis of Gene Copy Number Variation and Gene Expression Date. Int. J. Comput. Their Appl. 19(2): 131-141 (2012) - [j4]Junbo Duan, Charles Soussen, David Brie, Jérôme Idier, Yu-Ping Wang:
On LARS/Homotopy Equivalence Conditions for Over-Determined LASSO. IEEE Signal Process. Lett. 19(12): 894-897 (2012) - [c8]Junbo Duan, Ji-Gang Zhang, Hongbao Cao, Hong-Wen Deng, Yu-Ping Wang:
Copy number variation estimation from multiple next-generation sequencing samples. BCB 2012: 555-557 - [c7]Hongbao Cao, Dongdong Lin, Junbo Duan, Yu-Ping Wang, Vince D. Calhoun:
Bio marker identification for diagnosis of schizophrenia with integrated analysis of fMRI and SNPs. BIBM 2012: 1-6 - [c6]Jingyao Li, Dongdong Lin, Hongbao Cao, Yu-Ping Wang:
Classification of multicolor fluorescence in-situ hybridization (M-FISH) image using structure based sparse representation model. BIBM 2012: 1-6 - [c5]Junbo Duan, Ji-Gang Zhang, Hong-Wen Deng, Yu-Ping Wang:
Detection of common copy number variation with application to population clustering from next generation sequencing data. EMBC 2012: 1246-1249 - [c4]Hongbao Cao, Shufeng Lei, Hong-Wen Deng, Yu-Ping Wang:
Identification of genes for complex diseases by integrating multiple types of genomic data. EMBC 2012: 5541-5544 - 2011
- [j3]Wenlong Tang, Hongbao Cao, Junbo Duan, Yu-Ping Wang:
A Compressed Sensing Based Approach for Subtyping of Leukemia from gene Expression Data. J. Bioinform. Comput. Biol. 9(5): 631-645 (2011) - [j2]