PUBLICATIONS

Book

1. S. Wan and M. W. Mak, "Machine Learning for Protein Subcellular Localization Prediction", De Gruyter, ISBN 978-1-5015-0150-0, 2015, Germany. [link]

2. S. Wan, Y. Fan, C. Jiang and S. Li, "Bioinformatics and Machine Learning for Cancer Biology", MDPI, ISBN 978-3-0365-4814-2, 2022, Switzerland. (edited book) [link] 

Journals (*: corresponding author)

3.  C. Zhang, X. Zhu, N. Peterson, J. Wang, and S. Wan*, “A Comprehensive Review on RNA Subcellular Localization Prediction”, 2025, arXiv, arXiv:2504.17162.

4. M. Sun, J. Wang, and S. Wan*, “Accurate Identification of Medulloblastoma Subtypes from Diverse Data Sources with Severe Batch Effects by RaMBat”, bioRxiv, 2025, 2025.02.24.640010.

5. B. Kennedy, M. Raza, S. Mirza, A. Rajan, F. Oruji, M. Storck, S. Lele, T. Reznicek, L. Li, J. Rowley, S. Wan, B. Mohapatra, H. Band, and V. Band, “ECD Co-Operates with ERBB2 to Promote Tumorigenesis through Upregulation of Unfolded Protein Response and Glycolysis”, bioRxiv, 2025, 2025.01.28.635284.

6. J. Fang, S. Singh, B. Wells, Q. Wu, H. Jin, L. Janke, S. Wan, J. Steele, J. P. Connelly, A. Murphy, R. Wang, A. M. Davidoff, M. Ashcroft, S. M. Pruett-Miller, and J. Yang, “The Context-Dependent Epigenetic and Organogenesis Programs Determine 3D vs. 2D Cellular Fitness”, eLife, 2025, vol. 14, pp. RP101299.

7. G. Feng, H. Xu, S. Wan, H. Wang, X. Chen, R. Magan, Y. Han, Y. Wei, and H. Gu, “Twelve Practical Recommendations for Developing and Applying Clinical Predictive Models”, The Innovation Medicine, 2024, vol. 2, no. 4, pp. 100105.

8. J. Feng#, M. Sun#, C. Liu, W. Zhang, C. Xu, J. Wang, G. Wang, and S. Wan*, “SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition”, Briefings in Functional Genomics, 2024, vol. 23, no. 6, pp. 879-890.

9. Z. Xu, L. Li, R. Liu, M. Azzam, S. Wan, and J. Wang, “Functional Connectivity Alterations in Cocaine Use Disorder: Insights from the Triple Network Model and the Addictions Neuroclinical Assessment Framework”, bioRxiv, 2024, 2024.11.12.623073.

10. M. Azzam, Z. Xu, R. Liu, L. Li, K. M. Soh, K. B. Challagundla, S. Wan, and J. Wang*, “A review of artificial intelligence-based brain age estimation and its applications for related diseases”, Briefings in Functional Genomics, 2024, elae042.

11. H. Xiao, J. Wang*, and S. Wan*, “WIMOAD: Weighted Integration of Multi-Omics Data for Alzheimer's Disease (AD) Diagnosis”, bioRxiv, 2024, 2024.09.25.614862.

12. L. Li, H. Xiao, X. Wu, Z. Tang, J. Khoury, J. Wang, and S. Wan*, “RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-Cell Acute Lymphoblastic Leukemia”, bioRxiv, 2024, 2024.09.24.614777.

13. H. Chen, Y. Xu, H. Lin, S. Wan*, and L. Luo*, “A Prognostic Framework for Predicting Lung Signet Ring Cell Carcinoma via a Machine Learning Based Cox Proportional Hazard Model”, Journal of Cancer Research and Clinical Oncology, 2024, vol. 150, no. 364, pp. 1-15. (JCR Q2; Rank: 79/214; IF: 2.7)

14. L. Li#, M. Sun#, J. Wang, and S. Wan*, "Multi-Omics Based Artificial Intelligence for Cancer Research", Advances in Cancer Research, 2024, vol. 163, pp. 303-356.

15. Z. Ahmed#, S. Wan#, F. Zhang#, and W. Zhong#, “Artificial Intelligence for Omics Data Analysis”, BMC Methods, 2024, vol. 1, no. 1, 4.

16. H. Xiao, Y. Zou, J. Wang, and S. Wan*, "A Review for Artificial Intelligence Based Protein Subcellular Localization", Biomolecules, 2024, vol. 14, no. 4, 409. (JCR Q1; Rank: 70/285; IF: 5.5). [link] [preprint]

17. J. Zeng, Y. Weng, T. Lai, L. Chen, Y. Li, Q. Huang, S. Zhong, S. Wan*, and L. Luo*, "Procyanidin alleviates ferroptosis and inflammation of LPS-induced RAW264.7 cell via the Nrf2/HO-1 pathway", Naunyn-Schmiedeberg's Archives of Pharmacology, 2023. (JCR Q2; Rank: 114/278; IF: 3.6) [link]

18. M. Sun#, L. Li#, H. Xiao#, J. Feng#, J. Wang, and S. Wan*, "Editorial: Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research, Volume II", Frontiers in Genetics, 2023, vol. 14, article 1256468. (JCR Q2; Rank: 48/176; IF: 4.599) [link]

19. H. J. Mallard, S. Wan, P. Nidhi, Y. D. Hanscom-Trofy, B. Mohapatra, N. T Woods, J. A. Lopez-Guerrero, A. Llombart-Bosch, I. Machado, K. Scotlandi, N. F. Kreiling, M. C. Perry, S. Mirza, D. W. Coulter, V. Band, H. Band, and G. Ghosal, "USP1 expression driven by EWS::FLI1 transcription factor stabilizes Survivin and mitigates replication stress in Ewing sarcoma", Molecular Cancer Research, 2023, vol. 21, no. 11, pp. 1186-1204. (JCR Q2; Rank: 72/241; IF: 5.2) [link]

20. W. Shi, S. Mirza, M. Kuss, B. Liu, A. Hartin, S. Wan, Y. Kong, B. Mohapatra, H. Band, V. Band* and B. Duan*, "Embedded bioprinting of breast tumor cells and organoids using low concentration collagen based bioinks", Advanced Healthcare Materials, 2023, 2300905. (JCR Q1; Rank: 8/96; IF: 10.0) [link]

21. Y. Liang, Z. Su, X. Mao, S. Wan* and L. Luo*, "Editorial: Ferroptosis as a Novel Therapeutic Target for Inflammation-Related Diseases", Frontiers in Pharmacology, 2023, vol. 14, article 1152326. (JCR Q1; Rank: 50/279; IF: 5.988) [link]

22. J. Wang and S. Wan*, “Editorial: Single Cell Meets Metabolism and Cancer Biology”, Frontiers in Oncology, 2023, vol. 13, article 1125186. (JCR Q2; Rank: 78/245; IF: 5.738) [link]

23. Y. Liu, J. Klein, R. Bajpai, Q. Tran, P. Kolekar, J. L. Smith, R. E. Ries, L. Dong, B. J. Huang, J. Wang, T. Alonzo, L. Tian, H. L. Mulder, K. Szlachta, T. I. Shaw, J. Ma, M. Walsh, G. Song, T. Westover, R. Autry, A. Gout, D. Wheeler, S. Wan, G. Wu, J. J. Yang, W. Evans, M. Loh, J. Easton, J. M. Klco, S. Meshinchi, P. A. Brown, S. M. Pruett-Miller and X. Ma, “Etiology of oncogenic fusions in 5,190 childhood cancers and its clinical and therapeutic implication”, Nature Communications, 2023, vol. 14, no. 1739, pp. 1-18. (JCR Q1; Rank: 4/72; IF: 14.919) [link]

24. T. Sakamoto, K. Batmanov, S. Wan, Y. Guo, L. Lai, R. B. Vega and D. P. Kelly, “The Nuclear Receptor ERR Cooperates with the Cardiogenic Factor GATA4 to Orchestrate Transcriptional Control of Cardiomyocyte Differentiation”, Nature Communications, 2022, vol. 13, no. 1991, pp. 1-20. (JCR Q1; Rank: 4/72; IF: 14.919) [link]

25. P. C. Chen, X. Han, T. Shaw, H. Sun, M. Niu, Z. Wang, Y. Jiao, B. Teubner, D. Eddins, L. Beloate, B. Bai, J. Mertz, Y. Li , Y. Fu , J. H. Cho , X. Wang , Z. Wu , S. Poudel , Z. F. Yuan, A. Mancieri, J. Low, H. M. Lee, M. Patton, L. Earls, E. Stewart, P. Vogel, S. Wan, G. Serrano, T. Beach, M. Dyer, R. Smeyne, T. Moldoveanu, T. Chen, G. Wu, S. Zakharenko, G. Yu and J. Peng, “Alzheimer’s disease-associated U1 snRNP splicing dysfunction causes neuronal hyperexcitability and cognitive impairment”, Nature Aging, 2022, vol. 2, pp. 923-940. (JCR Q1; Rank: 1/69; IF: 16.6) [link]

26. S. Wan* and J. Wang*, “A Sequence Obfuscation Method for Protecting Personal Genomic Privacy”, Frontiers in Genetics, 2022, vol. 13, article 876686. (JCR Q2; Rank: 48/176; IF: 4.599) [link]

27. W. Qi, W. Rosikiewicz, Z. Yin, B. Xu, H. Jiang, S. Wan, Y. Fan, G. Wu and L. Wang, “Genomic profiling identifies genes and pathways dysregulated by HEY1-NCOA2 fusion and shed a light on mesenchymal chondrosarcoma tumorigenesis”, Journal of Pathology, 2022, vol. 257, no. 5, pp. 579-592. (JCR Q1; Rank: 5/77; IF: 7.996) [link]

28. S. Wan*, C. Jiang, S. Li and Y. Fan, “Special Issue on Bioinformatics and Machine Learning for Cancer Biology”, Biology, 2022, vol. 11, no. 3, 361. (JCR Q1; Rank: 16/93; IF: 5.079) [link]

29. V. Honnell, J. Norrie, A. Patel, C. Ramirez, J. Zhang, K. Lai, S. Wan and M. A. Dyer, "Identification of a Modular Super-Enhancer in Murine Retinal Development", Nature Communications, 2022, vol. 13, no. 253, pp. 1-13. (JCR Q1; Rank: 4/72; IF: 14.919) [link]

30. R. Wang, X. Zheng, J. Wang, S. Wan, M. H. Wong, K. S. Leung, L. Cheng, “Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia”, Briefings in Bioinformatics, 2022, vol. 23, no. 2, bbac002. (JCR Q1; Rank: 3/78; IF: 11.622) [link]

31. C. Jiang, S. Wan, P. Hu, Y. Li and S. Li, “Editorial: Transcriptional Regulation in Metabolism and Immunology”, Frontiers in Genetics, 2022, vol. 13, article 845697. (JCR Q2; Rank: 48/176; IF: 4.599) [link]

32. S. Singh, W. Quarni, M. Goralski, S. Wan, H. Jin, L. A. Van de Velde, J. Fang, R. Sing, Y. Fan, M. Johnson, W. Akers, P. Murray, P. G. Thomas, D. Nijhawan, A. M. Davidoff, J. Yang, “Targeting the spliceosome through RBM39 degradation results in exceptional responses in high-risk neuroblastoma models”, Science Advances, 2021, vol. 7, no. 47, eabj5405. (JCR Q1; Rank: 5/73; IF: 14.136) [link]

33. A. Lavado, R. Gangwar, J. Pare, S. Wan, Y. Fan and X. Cao, “YAP/TAZ maintain the proliferative capacity and structural organization of radial glial cells during brain development”, Developmental Biology, 2021, vol. 480, pp. 39-49. (JCR Q2; Rank: 15/41; IF: 3.582) [link]

34. S. Wan, J. Kim, and K. J.  Won, "SHARP: Hyper-Fast and Accurate Processing of Single-Cell RNA-seq Data via Ensemble Random Projection", Genome Research, 2020, vol. 30, pp. 205-213. [link] [preprint] (JCR Q1; Rank: 6/177; IF: 11.093)

35. T. Sakamoto, T. Matsuura, S. Wan, D. Ryba, J. Kim, K. J. Won, L. Lai, C. Petucci, N. Petrenko, K. Musunuru, R. Vega, D. Kelly, “A Critical Role for Estrogen-Related Receptor Signaling in Cardiac Maturation”, Circulation Research, 2020, vol. 126, pp. 1685-1702. (JCR Q1; Rank: 5/138; IF: 14.467) [link]

36. B. Ahn, S. Wan, N. Jaiswal, R. Vega, D. E. Ayer, P. M. Titchenell, X. Han, K. J. Won, and D. P. Kelly, "MondoA Drives Muscle Lipid Accumulation and Insulin Resistance", JCI Insight, 2019, vol. 4, no. 15, pp. e129119. [link] (JCR Q1; Rank: 14/138; IF: 6.205)

37. S. Wan and M. W. Mak, "Predicting Subcellular Localization of Multi-Location Proteins by Improving Support Vector Machines with an Adaptive-Decision Scheme", International Journal of Machine Learning and Cybernetics, 2018, vol. 9, pp. 399-411. [link] (JCR Q1; Rank: 31/134; IF: 3.844)

38. J. Q. Wang, C. Zhang, S. Wan, and G. Peng, "Is Congenital Amusia a Disconnection Syndrome? A Study Combining Tract- and Network-Based Analysis", Frontiers in Human Neuroscience, 2017, vol. 11, pp. 473. doi: 10.3389/fnhum.2017.00473. eCollection 2017. [link] (Impact Factor: 2.870, Ranking: 21/77)

39. S. Wan, M. W. Mak, and S. Y. Kung, "Gram-LocEN: Interpretable Prediction of Subcellular Multi-Localization of Gram-Positive and Gram-Negative Bacterial Proteins", Chemometrics and Intelligent Laboratory Systems, 2017, vol. 162, pp. 1-9. [link] (JCR Q1; Rank: 11/123; IF: 2.895)

40. S. Wan, M. W. Mak, and S. Y. Kung, "FUEL-mLoc: Feature-Unified Prediction and Explanation of Multi-Localization of Cellular Proteins in Multiple Organisms", Bioinformatics, 2017, vol. 33, pp. 749-750. [link] (JCR Q1; Rank: 3/58; IF: 6.937)

41. S. Wan, M. W. Mak, and S. Y. Kung, "Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins", Journal of Proteome Research, 2016, vol. 15, pp. 4755-4762. [link] (JCR Q1; Rank: 17/78; IF: 4.466)

42. S. Wan, M. W. Mak, and S. Y. Kung, "Benchmark Data for Identifying Multi-Functional Types of Membrane Proteins", Data in Brief, 2016, vol. 8, pp. 105-107. [link] (JCR Q2; Rank: 58/128; IF: 1.2)

43. S. Wan, M. W. Mak, and S. Y. Kung, "Mem-ADSVM: A Two-Layer Multi-Label Predictor for Identifying Multi-Functional Types of Membrane Proteins", Journal of Theoretical Biology, 2016, vol. 398, pp. 32-42. [link] (JCR Q1; Rank: 13/57; IF: 2.113)

44. S. Wan, M. W. Mak, and S. Y. Kung, "Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction", IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, vol. 14, pp. 212-224. [link] (JCR Q1; Rank: 15/125; IF: 3.710)

45. S. Wan, M. W. Mak, and S. Y. Kung, "Sparse Regressions for Predicting and Interpreting Subcellular Localization of Multi-Label Proteins", BMC Bioinformatics, 2016, 17:97. [link] (JCR Q1; Rank: 9/59; IF: 3.242)

46. S. Wan, M. W. Mak, and S. Y. Kung, "Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets", IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016, vol. 13, pp. 706-718. [link] (JCR Q1; Rank: 15/125; IF: 3.710)

47. S. Wan, M. W. Mak, and S. Y. Kung, "mLASSO-Hum: A LASSO-Based Interpretable Human-Protein Subcellular Localization Predictor", Journal of Theoretical Biology, 2015, vol. 382, pp. 223-234. [link] (JCR Q1; Rank: 13/57; IF: 2.113)

48. S. Wan, M. W. Mak, and S. Y. Kung, " mPLR-Loc: An Adaptive-decision Multi-label Classifier Based on Penalized Logistic Regression for Protein Subcellular Localization Prediction", Analytical Biochemistry, 2015, vol. 473, pp. 14-27. [link]  (JCR Q2; Rank: 33/86; IF: 2.877)

49. S. Wan, M. W. Mak, and S. Y. Kung, "R3P-Loc: A Compact Multi-label Predictor Using Ridge Regression and Random Projection for Protein Subcellular Localization", Journal of Theoretical Biology, 2014, vol.360, pp. 34-45. [link] (JCR Q1; Rank: 13/57; IF: 2.113)

50. S. Wan, M. W. Mak, and S. Y. Kung, "HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins", PLoS ONE, 2014,  9(3): e89545. [link]  (JCR Q1; Rank: 15/64; IF: 2.806)

51. S. Wan, M. W. Mak, and S. Y. Kung, " Semantic Similarity over Gene Ontology for Multi-label Protein Subcellular Localization ", Engineering, 2013, vol. 5, pp. 68-72. [pdf] [link] (also presented in 2013 International Conference on Bioinformatics and Biomedical Engineering (iCBBE'2013), Beijing, China, Sep. 2013)

52. S. Wan, M. W. Mak, and S. Y. Kung, "GOASVM: A Subcellular Location Predictor by Incorporating Term-Frequency Gene Ontology into the General Form of Chou’s Pseudo-Amino Acid Composition", Journal of Theoretical Biology, 2013, vol. 323, pp. 40–48. [link] (JCR Q1; Rank: 13/57; IF: 2.113)

53. S. Wan, M. W. Mak, and S. Y. Kung, "mGOASVM: Multi-label Protein Subcellular Localization Based on Gene Ontology and Support Vector Machines", BMC Bioinformatics, 2012, 13:290. [link] (JCR Q1; Rank: 9/59; IF: 3.242) (highly accessed)
    
    Conference Papers

54. S. Wan, J. Kim, Y. Fan and K. J. Won, “Processing millions of single cells by SHARP”, The 11th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2020), virtual online, Sep. 2020. (Highlights) [link]

55. S. Wan, J. Kim, and K. J. Won, “Hyper-Fast and Accurate Clustering of Ultra-Large-Scale Single-Cell Data with Ensemble Random Projection”, The 2020 International Conference on Machine Learning (ICML) Workshop on Computational Biology, virtual online, Jul. 2020. (Highlights) [paper] [video slides]

56. S. Wan, M. W. Mak, and S. Y. Kung, "Protecting Genomic Privacy by a Sequence-Similarity Based Obfuscation Method", 2017, arXiv preprint arXiv: 1708.02629. [link]

57. M. AI, S. Wan and S. Y. Kung, "Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection", 2017, arXiv preprint arXiv:1702.07976. [link] 

58. S. Wan, M. W. Mak, B. Zhang, Y. Wang and S. Y. Kung, "Ensemble Random Projection for Multi-label Classification with Application to Protein Subcellular Localization", 2014 IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP'14), Florence, Italy, May 2014, pp. 5999-6003. [pdf] [link]

59. S. Wan, M. W. Mak, B. Zhang, Y. Wang and S. Y. Kung, "An Ensemble Classifier with Random Projection for Predicting Multi-label Protein Subcellular Localization", The 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM'2013), Shanghai, China, Dec. 2013, pp. 35-42. [link]

60. S. Wan, M. W. Mak, and S. Y. Kung, "Adaptive Thresholding for Multi-Label SVM Classification with Application to Protein Subcellular Localization Prediction",2013 IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP'13), Vancouver, Canada, May 2013, pp. 3547-3551. [pdf] [link]

61. S. Wan, M. W. Mak, and S. Y. Kung, "GOASVM: Protein Subcellular Localization Prediction Based on Gene Ontology Annotation and SVM", 2012 IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP'12), Kyoto, Japan, Mar. 2012, pp. 2229-2232. [link]

62. S. Wan, M. W. Mak, and S. Y. Kung, "Protein Subcellular Localization Prediction Based on Profile Alignment and Gene Ontology", 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP'11), Beijing, China, Sep. 2011, pp. 1-6. [link]

63. S. Wan, C. Yao, Y. Hu, G. Zhang ,“A Method of Continuous Data Flow Embedded within Speech Signals”, The 2-nd International Conference on Signal Acquisition and Processing (ICSAP’10), Bangalore, India, Feb. 2010, pp. 362-365. [link]
    
    Conference Abstracts

64. M. Sun, J. Wang and S. Wan*, “iS3RGs: Discriminating Diverse Medulloblastoma Subtypes by Leveraging Heterogenous Transcriptome Data with Batch Effects”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 5022-5022. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

65. L. Li, J. Khoury, J. Wang and S. Wan*, “AttentionAML: An Attention-Based Deep Learning Model for Accurate Identification of Childhood Acute Myeloid Leukemia Subtypes”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 7425-7425. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

66. N. Peterson, M. Sun, J. Wang and S. Wan*, “Identifying Pancreatic Cancer Subtypes by a Novel Meta-Learning Model”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 1104-1104. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

67. X. Wu, J. Wang and S. Wan*, “Combining Random Projection and Stacking Learning for NSCLC Subtype Classification Based on Transcriptomic Data”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 1098-1098. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

68. M. J. Baek, L. Li, J. Wang, V. Band and S. Wan*, “A Weighted Multi-Modal Transfer Learning Model for Alleviating Racial Disparities in Breast Cancer”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 3620-3620. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

69. B. Kennedy, M. Raza, S. Mirza, A. Rajan, F. Oruji, Ma. Storck, S. Lele, T. Reznicek, L. Li, J. Rowley, S. Wan, B. Mohapatra, H. Band and V. Band*, “ECD Co-Operates with ERBB2 to Promote Tumorigenesis through Upregulation of Unfolded Protein Response and Glycolysis”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 267-267. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

70. B. Mohapatra, A. Bhat, M. Raza, H. Luan, S. Shrestha, S. Kolluru, M. Storck, L. Li, F. Kuo, S. Leit, F. Qiu, S. Lele, D. Ciccone, C. Loh, S. Wan, V. Band and H. Band*, “Targeting CBL and CBLB Ubiquitin Ligases to Exhaust Cancer Stem Cells in Metastatic Breast Cancer”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 5697-5697. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

71. R. Liu, S. Wang, S. Wan and J. Wang*, “Enhancing Prostate Pelvic Multimodality Data Generating with Conditional Generative Models: A Pix2Pix-Based Approach for MRI-to-PET Synthesis”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 5016-5016. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

72. M. Azzam, H. Leuva, M. Zhou, B. Teply, R. Bergan, S. Bates, S. Wan, A. Fojo and J. Wang*, “Development of G-Rate Based Random Forest Machine Learning Model to Predict Overall Survival for Patients with Metastatic Prostate Cancer”, Cancer Research, 2025, vol. 85 (8_Supplement_1), pp. 6314-6314. (presented in AACR Annual Meeting 2025, Chicago, IL, Apr. 2025)

73. M. Sun, J. Wang and S. Wan*, “Identifying Antimicrobial Peptides Based on Proportionalized Split Amino Acid Compositions”, The 2024 International Conference on Intelligent Biology and Medicine (ICIBM 2024), Houston TX, Sep. 2024.

74. M. Sun, J. Wang and S. Wan*, “Discriminating Diverse Medulloblastoma Subtypes by Leveraging Heterogenous Transcriptome Data with Batch Effects”, The 2024 International Conference on Intelligent Biology and Medicine (ICIBM 2024), Houston TX, Sep. 2024.

75. L. Li, J. Khoury, J. Wang and S. Wan*, “Pediatric Acute Myeloid Leukemia Classification via Multi-Modal Ensemble Learning”, The 2024 International Conference on Intelligent Biology and Medicine (ICIBM 2024), Houston TX, Sep. 2024.

76. H. Xiao, J. Wang and S. Wan*, “WIMOAD: Weighted Integration of Multi-Omics Data for Alzheimer’s Disease (AD) Diagnosis”, The 2024 International Conference on Intelligent Biology and Medicine (ICIBM 2024), Houston TX, Sep. 2024.

77. M. Sun, J. Wang and S. Wan*, “iS3RGs: Discriminating Diverse Medulloblastoma Subtypes by Leveraging Heterogenous Transcriptome Data with Batch Effects”, Pediatric Cancer Research Symposium, Omaha, NE, Aug. 2024.

78. L. Li, J. Khoury, J. Wang and S. Wan*, “AttentionAML: An Attention-Based Deep Learning Model for Identifying Acute Myeloid Leukemia Subtypes”, Pediatric Cancer Research Symposium, Omaha, NE, Aug. 2024.

79. L. Li, H. Xiao, J. Khoury, J. Wang and S. Wan*, “RanBALL: Identifying B-Cell Acute Lymphoblastic Leukemia Subtypes Based on an Ensemble Random Projection Model”, Cancer Research, 2024, vol. 84 (6_Supplement), pp. 4907-4907. (presented in AACR Annual Meeting 2024, San Diego, CA, Apr. 2024)

80. L. Li, J. Wang and S. Wan*, “Reducing Health Disparities for Prostate Adenocarcinoma by Integrating Multi-Omics Data via a Multi-Modal Transfer Learning Approach”, Cancer Research, 2024, vol. 84 (6_Supplement), pp. 4800-4800. (presented in AACR Annual Meeting 2024, San Diego, CA, Apr. 2024)

81. L. Li, H. Xiao and S. Wan*, “B-Cell Acute Lymphoblastic Leukemia Subtype Identification with an Ensemble Random Projection-Based Machine Learning Model”, CHRI Scientific Conference, Omaha, NE, Nov. 2023.

82. L. Li and S. Wan*, “Integrating Multi-Omics Data by a Multi-Modal Transfer Learning Model to Reduce Healthcare Disparities for Kidney Renal Clear Cell Carcinoma”, CHRI Scientific Conference, Omaha, NE, Nov. 2023.

83. J. Feng, M. Sun, W. Zhang, G. Wang and S. Wan*, “SAMP: An Accurate Ensemble Model Based on Proportionalized Split Amino Acid Composition for Identifying Antimicrobial Peptides”, Antimicrobial Peptides: Yesterday, Today and Tomorrow, Omaha, NE, Oct. 2023.

84. L. Li, H. Xiao and S. Wan*, “RanBALL: an Ensemble Random Projection-Based Model for Identifying B-Cell Acute Lymphoblastic Leukemia Subtypes”, PCRG Symposium 2023, Omaha, NE, Aug. 2023. 

85. W. Qi, W. Rosikiewicz, Z. Yin, B. Xu, S. Wan, Y. Fan, G. Wu and L. Wang, “RNA-seq and ChIP-seq profiling identifies genes and pathways dysregulated by hey1-ncoa2 fusion and shed a light on mesenchymal chondrosarcoma tumorigenesis”, AACR Annual Meeting 2021, Philadelphia, PA, Apr. 2021.

86. T. Sakamoto, S. Wan, K. Batmanov, and D. P. Kelly, "The Estrogen-related Receptor (ERR) Drives Cardiac Myocyte Maturation in Cooperation With GATA4", Circulation Research, 2020, vol. 127 (Suppl_1), pp. A222-A222. 

87. S. Wan, J. Kim, K. J. Won, and Y. Fan, "Hyper-Fast and Accurate Clustering of Ultra-Large-Scale Single-Cell Data with Ensemble Random Projection", Cell Symposia: The Conceptual Power of Single-Cell Biology, San Francisco, CA, USA, Apr. 2020. (postponed due to COVID-19 outbreak)

88. T. Sakamoto, S. Wan, K. J. Won, and D. P. Kelly, "The Estrogen-related Receptor Coordinates Transcription Of Genes Involved In Mitochondrial And Contractile Maturation In Human Induced Pluripotent Stem Cell-derived Cardiac Myocytes", Circulation, 2019, vol. 140 (Suppl_1), pp. A11803-A11803. (presented in American Heart Association Scientific Session (AHA2019), Philadelphia, PA, USA, Nov. 2019) [link]

89. T. R. Matsuura, T. Sakamoto, D. M. Ryba, S. Wan, and D. P. Kelly, "Estrogen-Related Receptor Signaling is Critical for Postnatal Cardiac Maturation", Circulation, 2019, vol. 140 (Suppl_1), A13898-A13898. (presented in American Heart Association Scientific Session (AHA2019), Philadelphia, PA, USA, Nov. 2019) [link]

90. B. Ahn, S. Wan, K. J. Won, N. Jaiswal, P. M. Titchenell, and D. P. Kelly, "MondoA Mediates Myocyte Lipid Accumulation and Insulin Resistance Driven by Chronic Nutrient Excess", American Diabetes Association's 79th Scientific Sessions (ADA2019), San Francisco, CA, USA, Jun. 2019. (oral)

91. S. Wan, J. Kim, and K. J. Won, "Hyper-Fast and Accurate Processing of Large-Scale Single-Cell Transcriptomics Data via Ensemble Random Projection", RECOMB/ISCB Conference on Regulatory & Systems Genomics with DREAM Challenges (RSG DREAM 2018), New York, USA, Dec. 2018. [link]