Jump to Journal, Conference Proceedings.

Journal

1. Kim, M., Min, E.J., Liu, K., Yan, J., Saykin, A.J., Moore, J.H., Long, Q. and Shen, L., 2022. Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics. Medical Image Analysis, 76, p.102297.
   

2. Cong, S., Yao, X., Xie, L., Yan, J. and Shen, L., 2022. Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts. Frontier in Genetics. 12:782953.
   

3. Li, J., Chen, F., Liang, H. and Yan, J., 2022. MoNET: an R package for multi-omic network analysis. Bioinformatics, 38(4), pp.1165-1167.
   

4. Xie, L., He, B., Varathan, P., Nho, K., Risacher, S.L., Saykin, A.J., Yan, J.†, (2021) Integrative -omics for discovery of network-level disease biomarkers: a case study in Alzheimer’s disease. Briefings in Bioinformatics. Accepted.
   

5. Zhang, L., Wang, L., Gao, J., Risacher, S.L., Yan, J., Li, G., Liu, T., Zhu, D. and Alzheimer’s Disease Neuroimaging Initiative, 2021. Deep fusion of brain structure-function in mild cognitive impairment. Medical image analysis, 72, p.102082.
   

6. Baloni, P., Funk, C.C., Yan, J., Yurkovich, J.T., Kueider-Paisley, A., Nho, K., Heinken, A., Jia, W., Mahmoudiandehkordi, S., Louie, G. and Saykin, A.J., (2020). Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer’s Disease. Cell Reports Medicine, 1(8), p.100138.
   

7. Xie, L., Varathan, P., Nho, K., Saykin, A.J., Salama, P. and Yan, J.†, (2020). Identification of functionally connected multi-omic biomarkers for Alzheimer’s disease using modularity-constrained Lasso. PloS one, 15(6), p.e0234748.
   

8. Upadhyaya, Y, Xie, L, Salama, P, Cao, S, Nho, K, Saykin, A. J and Yan, J.† (2020). Differential co-expression analysis reveals early stage transcriptomic decoupling in Alzheimer’s disease. BMC medical genomics, 13, 1-10.
   

9. Yao, X., Yan, J., Shen, L., (2019) Applications of imaging genomics beyond oncology. Radiomics and Radiogenomics: Technical Basis and Clinical Applications. CRC Press. (Book Chapter)
   

10. Chasioti, D., Yan, J.†, Nho, K., Saykin, A. J. (2019). Progress in Polygenic Composite Scores in Alzheimer’s and Other Complex Diseases. Trends in Genetics.
    

11. Yan, J.†, Raja V, V., Huang, Z., Amico, E., Nho, K., Fang, S., Sporns, O., Wu, Y.-C., Saykin, A. J., Joaquin, G., Shen, L. (2018). Brain-wide structural connectivity alterations under the control of Alzheimer risk genes. International Journal of Computational Biology and Drug Design.
    

12. Yan, J., Risacher, S. L., Shen, L., Saykin, A. J. (2018). Network approaches to systems biology analysis of complex disease: Integrative methods for multi-omics data. Briefings in Bioinformatics.
    

13. Zigon, B., Li, H., Yao, X., Fang, S., Hasan, M. A., Yan, J., Moore, J. H., Saykin, A. J., Shen, L. (2018). GPU Accelerated Browser for Neuroimaging Genomics. Neuroinformatics, 16(3-4), 393-402.
    

14. Wang, X., Chen, H., Yan, J., Nho, K., Risacher, S. L., Saykin, A. J., Shen, L., Huang, H. (2018). Quantitative trait loci identification for brain endophenotypes via new additive model with random networks. Bioinformatics (Oxford, England), 34(17), i866-i874.
    

15. Wang, X., Yan, J., Yao, X., Kim, S., Nho, K., Risacher, S. L., Saykin, A. J., Shen, L., Huang, H. (2018). Longitudinal Genotype-Phenotype Association Study through Temporal Structure Auto-Learning Predictive Model. Journal of computational biology: a journal of computational molecular cell biology, 25(7), 809-824.
    

16. Du, L., Liu, K., Zhang, T., Yao, X., Yan, J., Risacher, S. L., Han, J., Guo, L., Saykin, A. J., Shen, L. (2017). A Novel SCCA Approach via Truncated l1-norm and Truncated Group Lasso for Brain Imaging Genetics. Bioinformatics.
    

17. Li, J., Zhang, Q., Chen, F., Meng, X., Liu, W., Chen, D., Yan, J., Kim, S., Wang, L., Feng, W., Saykin, A. J., Liang, H., Shen, L. (2017). Genome-wide association and interaction studies of CSF T-tau/Abeta42 ratio in ADNI cohort. Neurobiol Aging, 57, 247.e1-247.e8.
    

18. Cong, W., Meng, X., Li, J., Zhang, Q., Chen, F., Liu, W., Wang, Y., Cheng, S., Yao, X., Yan, J., Kim, S., Saykin, A. J., Liang, H., Shen, L. (2017). Genome-wide network-based pathway analysis of CSF t-tau/Abeta1-42 ratio in the ADNI cohort. BMC Genomics, 18(1), 421.
    

19. Hao, X., Li, C., Yan, J., Yao, X., Risacher, S. L., Saykin, A. J., Shen, L., Zhang, D. (2017). Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis. Bioinformatics, 33(14), i341-i349.
    

20. Yao, X., Yan, J., Ginda, M., Borner, K., Saykin, A. J., Shen, L. (2017). Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer’s disease neuroimaging initiative. PLoS One, 12(11), e0186095.
    

21. Hao, X., Li, C., Du, L., Yao, X., Yan, J., Risacher, S. L., Saykin, A. J., Shen, L., Zhang, D. (2017). Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease. Sci Rep, 7, 44272.
    

22. Du, L., Liu, K., Yao, X., Yan, J., Risacher, S. L., Han, J., Guo, L., Saykin, A. J., Shen, L. (2017). Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty. Sci Rep, 7(1), 14052.
    

23. Yao, X., Yan, J., Liu, K., Kim, S., Nho, K., Risacher, S. L., Greene, C. S., Moore, J. H., Saykin, A. J., Shen, L. (2017). Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules. Bioinformatics, 33(20), 3250-3257.
    

24. Yao, X., Yan, J., Kim, S., Nho, K., Risacher, S. L., Inlow, M., Moore, J. H., Saykin, A. J., Shen, L. (2017). Two-dimensional enrichment analysis for mining high-level imaging genetic associations. Brain Inform, 4(1), 27-37.
    

25. Hao, X., Yao, X., Yan, J., Risacher, S. L., Saykin, A. J., Zhang, D., Shen, L., Initiative, A. D. N., others (2016). Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease. Neuroinformatics, 14(4), 439–452.
    

26. Song, A., Yan, J., Kim, S., Risacher, S. L., Wong, A. K., Saykin, A. J., Shen, L., Greene, C. S. (2016). Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts. BioData mining, 9(1), 3.
    

27. Du, L., Huang, H., Yan, J., Kim, S., Risacher, S. L., Inlow, M., Moore, J. H., Saykin, A. J., Shen, L., Initiative, A. D. N., others (2016). Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method. Bioinformatics, btw033.
    

28. Yan, J., Du, L., Yao, X., Shen, L. (2016). Machine learning in brain imaging genomics. Machine Learning and Medical Imaging. Elsevier.
    

29. Yan, J., Li, T., Wang, H., Huang, H., Wan, J., Nho, K., Kim, S., Risacher, S. L., Saykin, A. J., Shen, L., others (2015). Cortical surface biomarkers for predicting cognitive outcomes using group l 2, 1 norm. Neurobiology of aging, 36, S185–S193.
    

30. Yan, J., Kim, S., Nho, K., Chen, R., Risacher, S. L., Moore, J. H., Saykin, A. J., Shen, L., Initiative, A. D. N., others (2015). Hippocampal transcriptome-guided genetic analysis of correlated episodic memory phenotypes in Alzheimer’s disease. Frontiers in genetics, 6.
    

31. Wan, J., Zhang, Z., Rao, B. D., Fang, S., Yan, J., Saykin, A. J., Shen, L. (2014). Identifying the neuroanatomical basis of cognitive impairment in Alzheimer’s disease by correlation-and nonlinearity-aware sparse Bayesian learning. IEEE transactions on medical imaging, 33(7), 1475–1487.
    

32. Yan, J., Du, L., Kim, S., Risacher, S. L., Huang, H., Moore, J. H., Saykin, A. J., Shen, L., Initiative, A. D. N., others (2014). Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm. Bioinformatics, 30(17), i564–i571.
    

33. Li, T., Xie, Z., Wu, J., Yan, J., Shen, L. (2013). Interactive object extraction by merging regions with k-global maximal similarity. Neurocomputing, 120, 610–623.
    

34. Wang, H., Nie, F., Huang, H., Yan, J., Kim, S., Nho, K., Risacher, S. L., Saykin, A. J., Shen, L., Initiative, A. D. N., others (2012). From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer’s disease relevant SNPs. Bioinformatics, 28(18), i619–i625.
    

Conference Proceedings

1. Wu, Z., Yan, J., Wang, K., Liu, X., Guo, Y., Zhi, D., Ruan, J., Zhao, Z. (2019). The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics with bigger data and wider applications. (Suppl 1 ed., vol. 20, pp. 80).
   

2. Upadhyaya, Y., Xie, L., Salama, P., Nho, K., Saykin, A. J., Yan, J.*†. Disruption of gene co-expression network along the progression of Alzheimer’s disease. 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 1-4).
   

3. Xie, L., Amico, E., Salama, P., Wu, Y.-C., Fang, S., Sporns, O., Saykin, A. J., Goni, Joaquin, Yan, J.*†, Shen, L. (2018). Heritability Estimation of Reliable Connectomic Features. MICCAI International Workshop on Connectomics in Neuroimaging (pp. 58–66).
   

4. Wang, X., Liu, K., Yan, J. *, Risacher, S. L., Saykin, A. J., Shen, L., Huang, H. (2017). Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-Learned Structured Low-Rank Model. Inf Process Med Imaging, 10265, 198-209.
   

5. Yan, J.*, Liu, K., Risacher, S. L., Nho, K., Saykin, A. J., Shen, L. (2017). Graph embedded sparse association model for joint identification of discriminative imaging proteomic markers and their associations. International Symposium on Biomedical Imaging.
   

6. Yan, J.*, Risacher, S. L., Nho, K., Saykin, A. J., Shen, L. (2017). Identification of discriminative imaging proteomics associations in Alzheimer’s disease via a novel sparse correlation model. Pacific Symposium on Biocomputing (vol. 22, pp. 94-104).
   

7. Du, L., Zhang, T., Liu, K., Yan, J.*, Yao, X., Risacher, S. L., Saykin, A. J., Han, J., Guo, L., Shen, L. (2017). Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach. Inf Process Med Imaging, 10265, 543-555.
   

8. Hao, X., Yan, J. *, Yao, X., Risacher, S. L., Saykin, A. J., Zhang, D., Shen, L. (2016). Diagnosis-Guided Method For Identifying Multi-Modality Neuroimaging Biomarkers Associated With Genetic Risk Factors In Alzheimer’s Disease. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (vol. 21, pp. 108).
   

9. Du, L., Zhang, T., Liu, K., Yao, X., Yan, J. *, Risacher, S. L., Guo, L., Saykin, A. J., Shen, L. (2016). Sparse Canonical Correlation Analysis via truncated ℓ 1-norm with application to brain imaging genetics. Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on (pp. 707–711).
   

10. Du, L., Yan, J., Kim, S., Risacher, S. L., Huang, H., Inlow, M., Moore, J. H., Saykin, A. J., Shen, L., others (2015). Gn-scca: Graphnet based sparse canonical correlation analysis for brain imaging genetics. International Conference on Brain Informatics and Health (pp. 275–284).
    

11. Gao, H., Cai, C., Yan, J., Yan, L., Cortes, J. G., Wang, Y., Nie, F., West, J., Saykin, A. J., Shen, L., others (2015). Identifying Connectome Module Patterns via New Balanced Multi-graph Normalized Cut. International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 169–176).
    

12. Yao, X., Yan, J., Kim, S., Nho, K., Risacher, S. L., Inlow, M., Moore, J. H., Saykin, A. J., Shen, L., others (2015). Two-dimensional enrichment analysis for mining high-level imaging genetic associations. International Conference on Brain Informatics and Health (pp. 115–124).
    

13. Du, L., Yan, J., Kim, S., Risacher, S. L., Huang, H., Inlow, M., Moore, J. H., Saykin, A. J., Shen, L. (2014). A novel structure-aware sparse learning algorithm for brain imaging genetics. International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 329–336).
    

14. Yan, J., Zhang, H., Du, L., Wernert, E. A., Saykin, A. J., Shen, L. (2014). Accelerating sparse canonical correlation analysis for large brain imaging genetics data. Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment (pp. 4).
    

15. Sheng, J., Kim, S., Yan, J., Moore, J., Saykin, A. J., Shen, L. (2014). Data synthesis and method evaluation for brain imaging genetics. Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on (pp. 1202–1205).
    

16. Wang, D., Wang, Y., Nie, F., Yan, J., Cai, W., Saykin, A. J., Shen, L., Huang, H. (2014). Human connectome module pattern detection using a new multi-graph minmax cut model. International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 313–320).
    

17. Yan, J., Huang, H., Kim, S., Moore, J., Saykin, A. J., Shen, L. (2014). Joint identification of imaging and proteomics biomarkers of Alzheimer’s disease using network-guided sparse learning. Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on (pp. 665–668).
    

18. Huang, H., Yan, J., Nie, F., Huang, J., Cai, W., Saykin, A. J., Shen, L. (2013). A new sparse simplex model for brain anatomical and genetic network analysis. International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 625–632).
    

19. Yan, J., Huang, H., Risacher, S. L., Kim, S., Inlow, M., Moore, J. H., Saykin, A. J., Shen, L. (2013). Network-guided sparse learning for predicting cognitive outcomes from MRI measures. International Workshop on Multimodal Brain Image Analysis (pp. 202–210).
    

20. Wang, D., Nie, F., Huang, H., Yan, J., Risacher, S. L., Saykin, A. J., Shen, L. (2013). Structural brain network constrained neuroimaging marker identification for predicting cognitive functions. Information processing in medical imaging. (vol. 23, pp. 536).
    

21. Wang, H., Nie, F., Huang, H., Yan, J., Kim, S., Risacher, S. L., Saykin, A. J., Shen, L. (2012). High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer’s disease progression prediction. Advances in Neural Information Processing Systems (pp. 1277–1285).
    

22. Li, T., Wana, J., Zhang, Z., Yan, J., Kim, S., Risacher, S. L., Fang, S., Beg, M. F., Wang, L., Saykin, A., others (2012). Hippocampus as a predictor of cognitive performance: comparative evaluation of analytical methods and morphometric measures. MICCAI Workshop on Novel Imaging Biomarkers for Alzheimer’s Disease and Related Disorders (NIBAD’12) (pp. 133–144).
    

23. Yan, J., Risacher, S. L., Kim, S., Simon, J. C., Li, T., Wan, J., Wang, H., Huang, H., Saykin, A. J., Shen, L. (2012). Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. International Workshop on Multimodal Brain Image Analysis (pp. 1–17).
    

24. Wan, J., Zhang, Z., Yan, J., Li, T., Rao, B. D., Fang, S., Kim, S., Risacher, S. L., Saykin, A. J., Shen, L. (2012). Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer’s disease. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 940–947).