Jingwen Yan
Jingwen Yan
Indiana University Purdue University Indianapolis
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Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data
J Yan, SL Risacher, L Shen, AJ Saykin
Briefings in bioinformatics 19 (6), 1370-1381, 2018
Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimer˘s disease
P Baloni, CC Funk, J Yan, JT Yurkovich, A Kueider-Paisley, K Nho, ...
Cell Reports Medicine 1 (8), 2020
Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method
L Du, H Huang, J Yan, S Kim, SL Risacher, M Inlow, JH Moore, AJ Saykin, ...
Bioinformatics 32 (10), 1544-1551, 2016
High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer's disease progression prediction
H Wang, F Nie, H Huang, J Yan, S Kim, S Risacher, A Saykin, L Shen
Advances in neural information processing systems 25, 2012
Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease
J Wan, Z Zhang, J Yan, T Li, BD Rao, S Fang, S Kim, SL Risacher, ...
2012 IEEE Conference on Computer Vision and Pattern Recognition, 940-947, 2012
From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs
H Wang, F Nie, H Huang, J Yan, S Kim, K Nho, SL Risacher, AJ Saykin, ...
Bioinformatics 28 (18), i619-i625, 2012
Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm
J Yan, L Du, S Kim, SL Risacher, H Huang, JH Moore, AJ Saykin, L Shen, ...
Bioinformatics 30 (17), i564-i571, 2014
Progress in polygenic composite scores in Alzheimer˘s and other complex diseases
D Chasioti, J Yan, K Nho, AJ Saykin
Trends in Genetics 35 (5), 371-382, 2019
Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation-and nonlinearity-aware sparse Bayesian learning
J Wan, Z Zhang, BD Rao, S Fang, J Yan, AJ Saykin, L Shen
IEEE transactions on medical imaging 33 (7), 1475-1487, 2014
Cortical surface biomarkers for predicting cognitive outcomes using group l2, 1 norm
J Yan, T Li, H Wang, H Huang, J Wan, K Nho, S Kim, SL Risacher, ...
Neurobiology of aging 36, S185-S193, 2015
A novel structure-aware sparse learning algorithm for brain imaging genetics
L Du, J Yan, S Kim, SL Risacher, H Huang, M Inlow, JH Moore, AJ Saykin, ...
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014
Deep fusion of brain structure-function in mild cognitive impairment
L Zhang, L Wang, J Gao, SL Risacher, J Yan, G Li, T Liu, D Zhu, ...
Medical image analysis 72, 102082, 2021
Mining outcome-relevant brain imaging genetic associations via three-way sparse canonical correlation analysis in Alzheimer˘s disease
X Hao, C Li, L Du, X Yao, J Yan, SL Risacher, AJ Saykin, L Shen, ...
Scientific reports 7 (1), 44272, 2017
Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis
X Hao, C Li, J Yan, X Yao, SL Risacher, AJ Saykin, L Shen, D Zhang, ...
Bioinformatics 33 (14), i341-i349, 2017
A novel SCCA approach via truncated 1-norm and truncated group lasso for brain imaging genetics
L Du, K Liu, T Zhang, X Yao, J Yan, SL Risacher, J Han, L Guo, AJ Saykin, ...
Bioinformatics 34 (2), 278-285, 2018
Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer˘s disease: a study of ADNI cohorts
A Song, J Yan, S Kim, SL Risacher, AK Wong, AJ Saykin, L Shen, ...
BioData mining 9, 1-8, 2016
Genome-wide association and interaction studies of CSF T-tau/Aâ42 ratio in ADNI cohort
J Li, Q Zhang, F Chen, X Meng, W Liu, D Chen, J Yan, S Kim, L Wang, ...
Neurobiology of aging 57, 247. e1-247. e8, 2017
Identifying multimodal intermediate phenotypes between genetic risk factors and disease status in Alzheimer˘s disease
X Hao, X Yao, J Yan, SL Risacher, AJ Saykin, D Zhang, L Shen, ...
Neuroinformatics 14, 439-452, 2016
Genetic interactions explain variance in cingulate amyloid burden: an AV-45 PET genome-wide association and interaction study in the ADNI cohort
J Li, Q Zhang, F Chen, J Yan, S Kim, L Wang, W Feng, AJ Saykin, H Liang, ...
BioMed research international 2015, 2015
Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules
X Yao, J Yan, K Liu, S Kim, K Nho, SL Risacher, CS Greene, JH Moore, ...
Bioinformatics 33 (20), 3250-3257, 2017
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