Journal Articles

  1. Cong S, Zhang M, et al. Graph-Sequence Enhanced Transformer for Template-Free Prediction of Natural Product Biosynthesis. Patterns, 2025. (JCR Q1).

  2. Hao J, Chen Z, Peng Q, et al. Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Construction and Evaluation. Journal of Medical Internet Research, 2025. (JCR Q1/中科院 TOP)

  3. Chen J, Yang Q, Dai Q, et al. FederEI: Federated Library Matching Framework for Electron Ionization Mass Spectrum Based Compound Identification[J]. Analytical Chemistry, 2024. (中科院/JCR Q1 TOP).

  4. Liang H, Luo H, Sang Z, Jia M, Jiang X, Wang Z, Cong S, Yao X. GREMI: an Explaninable Multi-omics Integration Framework for Enhanced Disease Prediction and Module Identification[J]. IEEE J Biomed Health Inform, 2024. (中科院/JCR Q1 TOP).  

  5. Yao X, Jiang X, Luo H, et al. MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder[J]. BioData Mining, 2024, 17(1): 9. (JCR Q1).

  6. Luo H, Liang H, Liu H, et al. TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction[J]. International Journal of Molecular Sciences, 2024, 25(3): 1655. (JCR Q1).

  7. Cong, S., Wang, H., Zhou, Y., Wang, Z., Yao, X., & Yang, C. (2024). Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry. Brain‐X, 2(2), e57.

  8. Zhu Y, Cong S, Zhang Q, et al. Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer with 18F-FDG PET/CT images[J]. Biomedical Physics & Engineering Express, 2024, 10(6): 065011. (JCR Q3).

  9. Yao X, Zhu Y, Huang Z, et al. Fusion of shallow and deep features from 18 F-FDG PET/CT for predicting EGFR-sensitizing mutations in non-small cell lung cancer[J]. Quantitative Imaging in Medicine and Surgery. (中科院/JCR Q2).

  10. Bian C, Xia N, Xie A, Cong S, & Dong Q (2023). Adversarially Trained Persistent Homology Based Graph Convolutional Network for Disease Identification Using Brain Connectivity. IEEE Transactions on Medical Imaging. (中科院/JCR Q1 TOP).

  11. Bo T, Li J, Hu G, Zhang G, Wang W, Lv Q, Zhao S, Ma J, Qin M, Yao X, Wang M, Wang G, Wang, Z. (2023). Brain-wide and cell-specific transcriptomic insights into MRI-derived cortical morphology in macaque monkeys. Nature Communications, 14(1), 1499. https://doi.org/10.1038/s41467-023-37246-w. (中科院/JCR Q1 TOP).

  12. Cong S, Yao X, Xie L, Yan J, Shen L, for the ADNI. (2022) Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts. Frontiers in Genetics. Accepted, Preview Version.(JCR Q2)

  13. Gutman BA, van Erp TGM, Alpert K, et al. (2022) A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium. Human Brain Mapping. 2022 Jan;43(1):352-72,https://doi.org/10.1002/hbm.25625.

  14. Meng X, Li J, Zhang Q, Chen F, Bian C, Yao X, Yan J, Xu Z, Risacher SL, Saykin AJ, Liang H, Shen L, for the ADNI. (2020) Multivariate genome-wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease. BMC Genomics, SI 21(11):896, https://doi.org/10.1186/s12864-020-07282-7.(JCR Q2)

  15. Peng B, Yao X, Risacher SL, Saykin AJ, Shen L, Ning X, for the ADNI (2020) Cognitive biomarker prioritization in Alzheimer's disease using brain morphometric data. BMC Med Inform Decis Mak. 2020 Dec 2;20(1):319, https://doi.org/10.1186/s12911-020-01339-z.(JCR Q3)

  16. Cong S, Yao X, Huang Z, Risacher SL, Nho K, Saykin AJ, Shen L, for the ADNI. (2020) Volumetric GWAS of medial temporal lobe structures identifies an ERC1 locus using ADNI high-resolution T2-weighted MRI data. Neurobiology of Aging, 95:81-93.https://doi.org/10.1016/j.neurobiolaging.2020.07.005 (JCR Q1)

  17. Du L, Liu F, Liu K, Yao X, Risacher SL, Han J, Guo L, Saykin AJ, Shen L, for the ADNI. (2020) Identifying diagnosis-specific genotype-phenotype associations via joint multi-task sparse canonical correlation analysis and classification. Bioinformatics (ISMB 2020 Issue), 36:i371-i379. [19.4% acceptance rate]

  18. Hao X, Bao Y, Guo Y, Yu M, Zhang D, Risacher SL, Saykin AJ, Yao X, Shen L. “Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease.” Medical Image Analysis. 2020.

  19. Du L, Liu K, Yao X, Risacher SL, Han J, Saykin AJ, Guo L, Shen L, for the ADNI. (2020) Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach. Medical Image Analysis, 61:101656.  https://doi.org/10.1016/j.media.2020.101656

  20. Hao X, Bao Y, Guo Y, Yu M, Zhang D, Risacher SL, Saykin AJ, Yao X, Shen L, for the ADNI. (2020) Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease. Medical Image Analysis, 60:101625. https://doi.org/10.1016/j.media.2019.101625

  21. Yao X, Cong S, Yan J, Risacher SL, Saykin AJ, Moore JH, Shen L. “Regional Imaging Genetic Enrichment Analysis.” Bioinformatics. 2020.4.15. b36(8):2554-2560. (JCR 1区, IF = 5.610, WOS: 000537473400030)

  22. Yao X, Risacher SL, Nho K, Saykin AJ, Wang Z, Shen L. “Targeted genetic analysis of cerebral blood flow imaging phenotypes implicates the INPP5D gene.” Neurobiology of Aging. 2019.9. 81:213-221. (JCR 1区, IF=4.347, WOS: 000484435300021)

  23. Yao X, Tsang T, Quinney S, Zhang P, Ning X, Shen L. “Mining and visualizing high-order directional drug interaction effects using the FAERS database.” BMC Medical Informatics and Decision Making. 2019.

  24. Li J, Chen F, Zhang Q, Meng X, Yao X, Risacher SL, Yan J, Saykin AJ, Liang H, Shen L, for the ADNI. “Genome-wide network-assisted association and enrichment study of amyloid imaging phenotype in Alzheimer’s disease.” Current Alzheimer Research. 2019.

  25. Du L, Liu K, Yao X, Risacher SL, Han J, Saykin AJ, Guo L, Shen L. “Multi-task sparse canonical correlation analysis with application to multi-modal brain imaging genetics.” IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2019.

  26. Yan J, Deng C, Wang X, Yao X, Shen L, Huang H. “Identifying imaging markers for predicting cognitive assessments using Wasserstein distances based matrix regression.” Frontiers in Neuroscience. 2019.

  27. Du L, Liu K, Zhu L, Yao X, Risacher SL, Guo L, Saykin AJ, Shen L. “Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort.” Bioinformatics. 2019

  28. Cong S, Risacher SL, West JD, Wu YC, Apostolova LG, Tallman E, et al. Volumetric comparison of hippocampal subfields extracted from 4-minute accelerate vs. 8-minute high-resolution T2-weighted 3T MRI scans. Brain Imaging and Behavior. 2018;12(6):1583–1595. (IF=3.418, JCR 2区).

  29. Chasioti D, Yao X, Zhang P, Lerner S, Quinney SK, Ning X, Lang L, Shen L. “Mining directional drug interaction effects on myopathy using the FAERS database.” IEEE Journal of Biomedical and Health Informatics. 2018.

  30. Wang X, Yan J, Yao X, Kim S, Nho K, Risacher SL, Saykin AJ, Shen L, Huang H. “Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model.” Journal of Computational Biology. 2018.

  31. Hao X, Yao X, Risacher SL, Saykin AJ, Yu J, Wang H, Tan L, Shen L, Zhang D. Identifying candidate genetic associations with MRI-derived AD-related ROI via tree-guided sparse learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2018.

  32. Zigon B, Li H, Yao X, Fang S, Hasan MA, Yan J, Moore JH, Saykin AJ, Shen L. “GPU accelerated browser for neuroimaging genomics.” Neuroinformatics. 2018.

  33. Du L, Liu K, Zhang T, Yao X, Yan J, Risacher SL, Han J, Guo L, Saykin AJ, Shen L. “A novel SCCA approach via truncated l1-norm and its application to brain imaging genetics.” Bioinformatics. 2018.

  34. Yao X, Yan J, Liu K, Kim S, Nho K, Risacher SL, Greene CS, Moore JH, Saykin AJ, Shen L. “Tissue-specific network-based genome-wide study of amygdala imaging phenotypes to identify functional interaction modules.” Bioinformatics. 2017.

  35. Yao X, Yan J, Kim S, Nho K, Risacher SL, Inlow M, Moore JH, Saykin AJ, Shen L. “Two-dimensional enrichment analysis for mining high-level imaging genetic associations.” Brain Informatics. 2017;4(1):27–37.

  36. Yao X, Yan J, Ginda M, Börner K, Saykin AJ, Shen L. “Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer’s disease neuroimaging initiative.” Plos One. 2017.

  37. Du L, Liu K, Yao X, Yan J, Risacher SL, Han J, Guo L, Saykin AJ, Shen L. “Pattern discovery in brain imaging genetics via SCCA modeling with a generic non-convex penalty.” Scientific Reports. 2017.

  38. Cong W, Meng X, Li J, Zhang Q, Chen F, Liu W, Wang Y, Cheng S, Yao X, Yan J, Kim S, Saykin AJ, Liang H, Shen L. “Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort.” BMC Genomics. 2017.

  39. Hao X, Li C, Yan J, Yao X, Risacher SL, Saykin A, Shen L, Zhang D. “Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.” Bioinformatics. 2017.

  40. Hao X, Li C, Du L, Yao X, Yan J, Risacher SL, Saykin A, Shen L, Zhang D. “Mining outcome-relevant brain imaging genetic associations via three-way sparse canonical correlation analysis in Alzheimer's disease”. Scientific Reports. 2017.

  41. Hao X, Yao X, Yan J, Risacher SL, Saykin AJ, Zhang D, Shen L. Identifying multimodal intermediate phenotypes between genetic risk factors and disease status in Alzheimer’s disease. Neuroinformatics. 2016.

  42. Saykin AJ, Shen L, Yao X, Kim S, Nho K, Risacher SL, Ramanan VK, Foroud TM, Faber KM, Sarwar N, Munsie LM, Hu X, Soares HD, Potkin SG, Thompson PM, Kauwe JS, Kaddurah-Daouk R, Green RC,  Toga AW, Weiner MW. Genetic Studies of Quantitative MCI and AD Phenotypes in ADNI: Progress, Opportunities, and Plans. Alzheimer's Dement. 2015. 11(7):792-814

  43. Yang J, Xu Y, Yao X and Chen G. “FNphasing: A novel fast heuristic algorithm for haplotype phasing based on flow network model.” IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2013.