Publication

Underline indicates the mentored student. Asterisk ‘*’ indicates the corresponding author.

Accepted

1.         R. Mavila, S. Jaiswal, R. Naswa, W. Yuwen, B. Erdly, D. Si*, “iCare – An AI-Powered Virtual Assistant for Mental Health”, IEEE ICHI 2024 (The 12th IEEE International Conference on Healthcare Informatics). UW Graduate School Conference Presentation Award

2.         R. Naswa, S. Jaiswal, R. Mavila, W. Yuwen, B. Erdly, D. Si*, “Assessing Empathy in Mental Health Caregivers using Conversational AI”, Poster in IEEE ICHI 2024 (The 12th IEEE International Conference on Healthcare Informatics). UW Graduate School Conference Presentation Award

3.         S. Chieh Cheng, S. Kopelovich*, D. Si, M. Divina, N. S. Gao, M. Y. Wang, J. J. Kim, Z. Li, J. Blank, R. M. Brian, D. Turkington, “Co-production of a Cognitive Behavioral Therapy for Psychosis Digital Platform for Family Caregivers of Individuals Impacted by Psychosis”, Journal of Technology in Behavioral Science.

4.         L. Zhang, J. Zhu, S. Wang, J. Hou, D. Si, R. Cao*, “AnglesRefine: refinement of 3D protein structures using Transformer based on torsion angles”, 22nd International Workshop on Data Mining in Bioinformatics (BIOKDD 2023)

5.         M. Korovnik, S. Wang, J. Zhu, K. Hippe, J. Hou, D. Si, K. Kishaba, R. Cao*, “SynthQA - Hierarchical Machine Learning-based Protein Quality Assessment”, the International Conference on Intelligent Biology and Medicine (ICIBM 2023)

Pre-prints

6.         R. Xing, T. Humphries*, D. Si*, “Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images”. arXiv:2112.12810

Journal Papers

7.         J. Chen, A. Zia, A. Luo, H. Meng, F. Wang, J. Hou, R. Cao, D. Si*, “Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration”, Briefings in Bioinformatics, 2024 Mar 27;25(3):bbae118, https://doi.org/10.1093/bib/bbae118. (Impact Factor 13.994)

8.         L. Zhang, S. Wang, J. Hou, D. Si, J. Zhu, R. Cao*, “ComplexQA: A Deep Graph Learning Approach for Protein Complex Structure Assessment”, Briefings in Bioinformatics, Volume 24, Issue 6, November 2023, bbad287, https://doi.org/10.1093/bib/bbad287. (Impact Factor 13.994)

9.         A. Nakamura, H. Meng, M. Zhao, F. Wang, J. Hou, R. Cao, D. Si*, “Fast and Automated Protein-DNA/RNA Macromolecular Complex Modeling from Cryo-EM Maps”, Briefings in Bioinformatics, 2023;, bbac632, https://doi.org/10.1093/bib/bbac632. (Impact Factor 13.994)

10.  D. Si*, J. Chen, A. Nakamura, L. Chang, H. Guan, “Smart De Novo Macromolecular Structure Modeling from Cryo-EM Maps”, Journal of Molecular Biology, 2023, 167967, ISSN 0022-2836, https://doi.org/10.1016/j.jmb.2023.167967. (Impact Factor 6.151)

11.      Y. Jia, N. McMichael, P. Mokarzel, B. Thompson, D. Si, and T. Humphries*, “Superiorization-inspired unrolled SART algorithm with U-Net generated perturbations for sparse-view and limited-angle CT reconstruction”, Physics in Medicine and Biology, 2022 Phys. Med. Biol. 67 245004 DOI: https://doi.org/10.1088/1361-6560/aca513. (Impact Factor 4.17)

12.      N. Ranno, D. Si*, “Neural Representations of Cryo-EM Maps and a Graph-Based Interpretation”, BMC Bioinformatics 23 (Suppl 3), 397 (2022). https://doi.org/10.1186/s12859-022-04942-1. (Impact Factor 3.17)

13.  F. Wang, C. H. Chan, V. Suciu, K. Mustafa, D. Si, A. Hochbaum, E. H. Egelman*, D. Bond, "Structure of Geobacter OmcZ filaments suggests extracellular cytochrome polymers evolved independently multiple times", eLife 11:e81551. https://doi.org/10.7554/eLife.81551. (Impact Factor 8.71)

14.      L. Chang, F. Wang*, K. Connolly, H. Meng, Z. Su, V. Cvirkaite-Krupovic, M. Krupovic, E. H. Egelman*, D. Si*, "DeepTracer-ID: De novo protein identification from cryo-EM maps", Biophysical Journal - Cell Press, 2022, https://doi.org/10.1016/j.bpj.2022.06.025. (Impact Factor 4.03)

15.  F. Wang, K. Mustafa, V. Suciu, K. Joshi, C.H. Chan, Z. Su, D. Si, A. I. Hochbaum*, E. H. Egelman*, D. R. Bond*. “Cryo-EM structure of an extracellular Geobacter OmcE cytochrome filament reveals tetrahaem packing”. Nature Microbiology, 2022 Jul 7. doi: 10.1038/s41564-022-01159-z. (Impact Factor 17.75)

16.      M. Pan*, Q. Zheng, T. Wang, L. Liang, J. Mao, C. Zuo, R. Ding, H. Ai, Y. Xie, D. Si, Y. Yu*, L. Liu*, M. Zhao*, “Structural insights into Ubr1-mediated N-degron polyubiquitination”. Nature (2021). https://doi.org/10.1038/s41586-021-04097-8 (Impact Factor 42.78)

17.  K. Hippe, C. Lilley, J. W. Berkenpas, C. C. Pocha, K. Kishaba, H. Ding, J. Hou, D. Si, R. Cao*, "ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features". Briefings in Bioinformatics, 2021, https://doi.org/10.1093/bib/bbab384 (Impact Factor 11.62)

18.  C. Hunt, S. Montgomery, J. W. Berkenpas, N. Sigafoos, J. C. Oakley, J. Espinosa, N. Justice, K. Kishaba, K. Hippe, D. Si, J. Hou, H. Ding, Renzhi Cao*, “Recent Progress of Machine Learning in Gene Therapy”, Current Gene Therapy 2021 Jun 22. doi: 10.2174/1566523221666210622164133 (Impact Factor 2.431)

19.      D. Si*, A. Nakamura, R. Tang, H. Guan, J. Hou, A. Firozi, R. Cao, K. Hippe, M. Zhao, “Artificial intelligence advances for de novo molecular structure modeling in cryo‐electron microscopy”. Wiley Interdisciplinary Reviews: Computational Molecular Science, 2021. https://doi.org/10.1002/wcms.1542 (Impact Factor 16.778)

20.      N. S. Dhillon, A. Sutandi, M. Vishwanath, M. M. Lim, H. Cao*, D. Si*, “A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram”. Sensors. 2021; 21(8):2779. https://doi.org/10.3390/s21082779 (Impact Factor 3.427)

21.      C. L Lawson*, …, J. Pfab, …, D. Si, et al., “Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge”. Nature Methods, 18, 156–164 (2021). https://www.nature.com/articles/s41592-020-01051-w (Impact Factor 34.975)

22.      J. Pfab, N. M. Phan, D. Si*, “DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes”, Proceedings of the National Academy of Sciences (PNAS), Jan 2021, 118 (2) e2017525118; DOI: 10.1073/pnas.2017525118. https://doi.org/10.1073/pnas.2017525118 (Impact Factor 10.620)

23.      D. Si*, S.A. Moritz, J. Pfab, et al., “Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps”. Scientific Reports, 10, 4282 (2020). https://www.nature.com/articles/s41598-020-60598-y (Impact Factor 4.576)

24.      J. Smith, M. Conover, N. Stephenson, J. Eickholt, D. Si, M. Sun, R. Cao*, “TopQA: A Topological Representation for Single-Model Protein Quality Assessment with Machine Learning”, International Journal of Computational Biology and Drug Design, Vol. 13, No. 1, 144-153, 2020. (Impact Factor 0.520)

25.      M. Conover, M. Staples, D. Si, M. Sun, R. Cao*, “AngularQA: Protein Model Quality Assessment with LSTM Networks”, Computational and Mathematical Biophysics, 7(1), pp. 1-9, 2019, doi:10.1515/cmb-2019-0001

26.      T. K. Avramov, D. Vyenielo, J. G. Blanco, S. Adinarayanan, J. Vargas*, D. Si*, “Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps”, Molecules, 24(6), 1181, 2019. DOI: 10.3390/molecules24061181. (Impact Factor 3.268)

27.      A. Ng, D. Si*, “Beta-Barrel Detection for Medium Resolution Cryo-EM Density Maps using Genetic Algorithms and Ray Tracing”, Journal of Computational Biology, 2018, 25:3, 326-336. DOI: 10.12017.0155 (Impact Factor 1.220)

28.      D. MacMichael, D. Si*, “Machine Learning Classification of Tree Cover Type and Application to Forest Management”, International Journal of Multimedia Data Engineering and Management, 9(1), 21 pages, 2017. DOI: 10.4018/IJMDEM.2018010101

29.      D. Si, J. He*, “Modeling Beta-Traces for Beta-Barrels from Cryo-EM Density Maps”, BioMed Research International, vol. 2017, Article ID 1793213, 9 pages, 2017. DOI:10.1155/2017/1793213 (Impact Factor 2.843)

30.  D. Si, J. He*, “Tracing Beta Strands Using StrandTwister from Cryo-EM Density Maps at Medium Resolutions”, Structure - Cell Press, Volume 22, Issue 11, p1665-1676, 2014. (Impact Factor 6.347)

31.  D. Si, S. Ji, K. Al Nasr, J. He*, “A Machine Learning Approach for the Identification of Protein Secondary Structure Elements from Electron Cryo-Microscopy Density Maps”, Biopolymers, Volume 97, Issue 9, p698-708, 2012. (Impact Factor 2.879)

32.  A. McKnight, D. Si, K. Al Nasr, A. Chernikov, N. Chrisochoides, J. He*, “Estimating loop length from CryoEM images at medium resolutions”, BMC Structural Biology, Volume 13, Suppl 1:S5, 2013. (Impact Factor 2.317)

33.  A. Biswas, D. Si, K. Al Nasr, D. Ranjan, M. Zubair, J. He*, “Improved Efficiency in Cryo-EM Secondary Structure Topology Determination from Inaccurate Data”, Journal of Bioinformatics and Computational Biology (JBCB), Volume 10, Issue 3, (16 pages), 2012. (Impact Factor 1.392)

Conference Proceedings

34.      H. Guan, D. Si*, “DeepTracer-Denoising: Deep Learning for 3D Electron Density Map Denoising”, 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA, 2022, pp. 2080-2087, doi: 10.1109/BIBM55620.2022.9994879. IEEE BIBM 2022 Student Travel Award

35.      J. Lee, S. Kopelovich, S. Cheng, and D. Si*, “Psychosis iREACH: Reach for Psychosis Treatment using Artificial Intelligence”, 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA, 2022, pp. 2913-2920, doi: 10.1109/BIBM55620.2022.9995430.

36.      M. Rahbar, R. K. Chauhan, P. N. Shah, R. Cao, D. Si, J. Hou*, “Deep Graph Learning to Estimate Protein Model Quality Using Structural Constraints from Multiple Sequence Alignments”, In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '22). Association for Computing Machinery, New York, NY, USA, Article 21, 1–10. https://doi.org/10.1145/3535508.3545558

37.  P. Saltz, S.Y. Lin, S. C. Cheng, D. Si*, “Dementia Detection using Transformers-Based Deep Learning and Natural Language Processing Models”, 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 2021, pp. 509-510, doi: 10.1109/ICHI52183.2021.00094.

38.  A. Sutandi, N. Dhillon, M. Lim, H. Cao, D. Si*, "Detection of Traumatic Brain Injury Using Single Channel Electroencephalogram in Mice," 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA, 2020, pp. 1-8, doi: 10.1109/SPMB50085.2020.9353651.

39.  T. Diem, D. Si*, Y. Chen*, “Predicting Traveler Next Choice of Activity with Location-Based Social Network Data”, PredictGIS'19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, November 2019, Pages 15–23, https://doi.org/10.1145/3356995.3364540

40.  D. Si*, S. C. Cheng, R. Xing, C. Liu and H. Y. Wu, "Scaling up Prediction of Psychosis by Natural Language Processing," 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 2019, pp. 339-347, doi: 10.1109/ICTAI.2019.00055.

41.  J. Pfab, D. Si*, “Automated Threshold Selection for Cryo-EM Density Maps”, BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, September 2019, Pages 161–166, https://doi.org/10.1145/3307339.3342190

42.  M. Staples, L. Chan, D. Si, K. Johnson, C. Whyte, R. Cao*, “Artificial Intelligence for Bioinformatics: Applications in Protein Folding Prediction”, 2019 IEEE Technology & Engineering Management Conference (TEMSCON), Atlanta, GA, USA, 2019, pp. 1-8. doi: 10.1109/TEMSCON.2019.8813656.

43.  T. J. Wroge, Y. Özkanca, C. Demiroglu, D. Si, D. C. Atkins and R. H. Ghomi, "Parkinson’s Disease Diagnosis Using Machine Learning and Voice," 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, 2018, pp. 1-7. doi: 10.1109/SPMB.2018.8615607

44.  T. Avramov, D. Si*, “Deep Learning for Resolution Validation of Three Dimensional Cryo-Electron Microscopy Density Maps”, In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB '18). ACM, New York, NY, USA, 669-674. DOI: https://doi.org/10.1145/3233547.3233712

45.  J. Yang, R. Cao, D. Si*, “EMNets: A Convolutional Autoencoder for Protein Surface Retrieval Based on Cryo-Electron Microscopy Imaging”, In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB '18). ACM, New York, NY, USA, 639-644. DOI: https://doi.org/10.1145/3233547.3233707

46.  Y.H. Lee*, M. Stiber, D. Si, “Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks”, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 348-351. DOI: 10.1109/EMBC.2018.8512358.

47.  Y. H. Hsu, D. Si*, “Cancer Type Prediction and Classification Based on RNA-sequencing Data”, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 5374-5377. DOI: 10.1109/EMBC.2018.8513521.

48.  B. Pittman, R. H. Ghomi, D. Si*, “Parkinson’s Disease Classification of mPower Walking Activity Participants”, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 4253-4256. DOI: 10.1109/EMBC.2018.8513409.

49.  J. Frierson, D. Si*, “Who’s Next: Evaluating Attrition with Machine Learning Algorithms and Survival Analysis,” in Big Data – BigData 2018, vol. 10968, Cham: Springer International Publishing, 2018, pp. 251–259.

50.  A. Ng, A. Odesile, and D. Si*, “GPU Accelerated Ray Tracing for the Beta-Barrel Detection from Three-Dimensional Cryo-EM Maps,” in Bioinformatics Research and Applications, vol. 10847, Cham: Springer International Publishing, 2018, pp. 217–226.

51.  S. Mason, F. Jagodzinski, B. Chen, M. Nissenson, D. Si, A. Valliani, A. Soni, X. Fang, W. Qiao, and A. Shehu. 2017. ACM-SIGBIO Undergraduate Research Highlight. ACM SIGBioinformatics Rec. 7, 2, Article 2 (October 2017), 3 pages. DOI: 10.1145/3148241.3148243.

52.  M. Nissenson, D. Si*, “Automated Protein Chain Isolation from 3D Cryo-EM Data and Volume Comparison Tool”, In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB '17). ACM, New York, NY, USA, 685-690. DOI: https://doi.org/10.1145/3107411.3107500 ACM SIGBIO Outstanding Undergraduate Research.

53.  P. Collins, D. Si*, “A Graph Based Method for the Prediction of Backbone Trace from Cryo-EM Density Maps”, In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB '17). ACM, New York, NY, USA, 691-697. DOI: https://doi.org/10.1145/3107411.3107501

54.  D. Macmichael, D. Si*, “Addressing Forest Management Challenges by Refining Tree Cover Type Classification with Machine Learning Models”, In Proceedings of the 18th IEEE International Conference on Information Reuse and Integration (IEEE-IRI ‘17), IEEE, San Diego, CA, 2017, pp. 177-183. doi: 10.1109/IRI.2017.89.

55.  T. Avramov, D. Si*, “Comparison of Feature Reduction Methods and Machine Learning Models for Breast Cancer Diagnosis”, In Proceedings of the International Conference on Compute and Data Analysis (ICCDA '17). ACM, New York, NY, USA, 69-74. DOI: https://doi.org/10.1145/3093241.3093290

56.  A. Burnett, D. Si*, “Prediction of Injuries and Fatalities in Aviation Accidents through Machine Learning”, In Proceedings of the International Conference on Compute and Data Analysis (ICCDA '17). ACM, New York, NY, USA, 60-68. DOI: https://doi.org/10.1145/3093241.3093288 Best Presentation Award.

57.  J. Ma, D. Si*, “Analyze NYC Transportation to Mitigate Speeding and Explore New Business Models Using Machine Learning”, In Proceedings of the International Conference on Compute and Data Analysis (ICCDA '17). ACM, New York, NY, USA, 75-80. DOI: https://doi.org/10.1145/3093241.3093291

58.  N. Grabaskas*, D. Si, “Anomaly Detection from Kepler Satellite Time-Series Data”. Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings (pp. 220-232). Cham: Springer International Publishing.

59.  R. Ogunnaike*, D. Si, “Prediction of Insurance Claim Severity Loss Using Regression Models”, Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings (pp. 233-247). Cham: Springer International Publishing.

60.  A. Ng, D. Si*, “Genetic Algorithm Based Beta-Barrel Detection for Medium Resolution Cryo-EM Density Maps”, Bioinformatics Research and Applications: 13th International Symposium, ISBRA 2017, Honolulu, HI, USA, May 29 – June 2, 2017, Proceedings (pp. 174-185). Cham: Springer International Publishing.

61.  R. Li, D. Si, T. Zeng, S. Ji, and J. He*, “Deep Convolutional Neural Networks for Protein Secondary Structure Detection in 3D Cryo-Electron Microscopy Images”, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, 2016, pp. 41-46.

62.  D. Si*, “Automatic Detection of Beta-barrel from Medium Resolution Cryo-EM Density Maps”, Proceedings of the ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB), p156-164, Seattle, WA, Oct 02-05, 2016.

63.  D. Si, J. He*, “Orientations of Beta-strand Traces and Near Maximum Twist”, Proceedings of the ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB), p690-694, Newport Beach CA, Sept 20-23, 2014.

64.  D. Si, J. He*, “Combining Image Processing and Modeling to Generate Traces of Beta-strands from Cryo-EM Density Images of Beta-barrels”, Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE-EMBC), p3941-3944, Chicago IL, Aug 26-30, 2014.

65.  J. He*, D. Si, “Towards De Novo Folding of Protein Structures from Cryo-EM 3D Images at Medium Resolutions”, Proceedings of the Robotics: Science and Systems Conference 2014, Workshop on Robotics Methods for Structural and Dynamic Modeling of Molecular Systems, UC Berkeley, July 12-13, 2014.

66.  D. Si, J. He*, “Modeling Protein Structure Features from Three Dimensional Cryo-EM Images”, Proceedings of the Modeling, Simulation, and Visualization Capstone Conference, P20-23, Suffolk VA, April 17, 2014.

67.  D. Si, J. He*, “Beta-sheet Detection and Representation from Medium Resolution Cryo-EM Density Maps”, Proceedings of the ACM International Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB), p764-770, Washington D.C., Sept 22-25, 2013.

68.  K. Al Nasr, L. Chen, D. Ranjan, M. Zubair, D. Si, J. He*, “A Constrained K-shortest Path Algorithm to Rank the Topologies of the Protein Secondary Structure Elements Detected in CryoEM Volume Maps”, Proceedings of the ACM International Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB), p749-755, Washington D.C., Sept 22-25, 2013.

69.  D. Si, H. E. Elsayed-Ali, W. Cao, O. Pakhomova, H. White, J. He*, “Simulation of the Localized 3-dimensional Reconstruction for Electron Cryo-tomography”, Proceedings of the Modeling, Simulation, and Visualization Capstone Conference, p100-103, Suffolk VA, April 11, 2013.

70.  A. McKnight, K. Al Nasr, D. Si, A. Chernikov, N. Chrisochoides, J. He*, “CryoEM Skeleton Length Estimation using a Decimate Curve”, Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (IEEE-BIBM), p109-113, Philadelphia PA, Oct 3-7, 2012.

71.  K. Al Nasr, L. Chen, D. Si, D. Ranjan, M. Zubair, J. He*, “Building the Initial Chain of the Proteins through De Novo Modeling of the Cryo-Electron Microscopy Volume Data at the Medium Resolutions”, Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB), p490-497, Orlando FL, Oct 7-10, 2012.

72.  A. Biswas, D. Si, K. Al Nasr, D. Ranjan, M. Zubair, J. He*, “A constraint dynamic graph approach to identify the secondary structure topology from cryoEM density data in presence of errors”, Proceeding of the IEEE International Conference of Bioinformatics and Biomedicine (IEEE-BIBM), p160-163, Atlanta GA, Nov 12-15, 2011.

Highlights & Posters & Abstracts

73.  D. Si, “Artificial Intelligence Advances For De Novo Molecular Structure Modeling In Cryo-EM And Next-Generation Molecular Biomedicine”, Highlight at ACM-BCB 2021

74.  Y.Jia, N. McMichael, P. Mokarzel, D. Si and T. Humphries. “Algorithm for limited angle CT reconstruction with U-net based regularization”, Accepted, Presentation at 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

75.  W. R. Kearns, N. Kaura, M. Divina, C. Vo, D. Si, T. Ward, and W. Yuwen. 2020. A Wizard-of-Oz Interface and Persona-based Methodology for Collecting Health Counseling Dialog. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–9. DOI:https://doi-org /10.1145/3334480.3382902

76.  S. Zhai, S.C. Cheng, D. Si, T.M. Ward, W. Erdly, W. Yuwen, “Participatory Design of a Tailored Self-Management Program for Caregivers of Children”. Western Institute of Nursing Annual Communicating Nursing Research Conference, Portland, OR, April 15-18, 2020.

77.  T, Humphries, D. Si, R. Xing, S. Coult, M Simms, “Comparison of deep learning approaches to low dose computed tomography using low intensity and sparse view data,” SPIE Medical Imaging Symposium, San Diego, February 16 - 21, 2019.

78.  R. Cao*, D. Si, et al. “Collaborative de novo protein structure prediction using stepwise fragment sampling with help of contact prediction and model selection based on deep learning techniques”, Critical Assessment of Techniques for Protein Structure Prediction (CASP) 13th meeting, Riviera Maya, Mexico, December 1-4, 2018.

79.  C. Liu, H. Y. Wu, R. Xing, T. Quang, S. C. Cheng, D. Si, “Computational Psychiatric Nursing Research: Scaling up the Prediction of Psychosis by Natural Language Processing”, Proceeding of the 11th International Conference on Early Intervention in Mental Health, Boston, Massachusetts, USA, 7th–10th October 2018.

80.  D. Si, “Intelligent 3D Cryo-EM Image Processing for Next Generation Biomedicine”, The BioImage Informatics (BII) conference 2017, Banff Canada, September 19 - 21, 2017.

81.  D. Si, “Intelligent 3D Cryo-EM Image Analysis for Next Generation Biomedicine”, 14th Annual Rocky Mountain Bioinformatics Conference, Aspen, December 8 - 10, 2016.

82.  D. Si, J. He, “Computational Development for Secondary Structure Detection from Three Dimensional Images of Cryo-Electron Microscopy”, Proceedings of the Modeling, Simulation, and Visualization Capstone Conference, p223, Norfolk VA, April 16, 2015.

83.  D. Si, J. He, “Tracing Beta-strands using Beta-twist from the Medium Resolution Cryo-EM Density Maps”, Cryo-EM 3D Image Analysis Symposium 2014, Lake Tahoe, Mar 12-15, 2014.

84.  K. Al Nasr, L. Chen, D. Si, D. Ranjan, M. Zubair, J. He, “Building the Initial Chain of the Proteins through De Novo Modeling of the Cryo-Electron Microscopy Volume Data at the Medium Resolutions”, The IEEE International Conference on Bioinformatics and Biomedicine (IEEE-BIBM), Philadelphia PA, Oct 3-7, 2012.

Book

85.  C. Si, D. Si, Q. Su, Q. Ai, “Algorithm Analysis and Design Techniques” (in Chinese), ISBN 978-7-5606-3900-0, Xidian University Press, January 2016.

Book Chapter

86.  D. Si*, H. Meng, J. Pfab, Y. Deng, Y. Xie, J. Tan, S. H. M. Chow, J. Chen, A. Jain. (2022). DeepTracer Web Service for Fast and Accurate De Novo Protein Complex Structure Prediction from Cryo-EM. Algorithms and Methods in Structural Bioinformatics. Computational Biology. Springer, Cham. https://doi.org/10.1007/978-3-031-05914-8_6

87.  J. He*, D. Si, M. Arab, “Detection of Secondary Structures from 3D Protein Images of Medium Resolutions and its Challenges”, Image and Graphics, Springer, Volume 9218, p147-155, 2015.

Ph.D. Dissertation

88.  D. Si, "Computational Development for Secondary Structure Detection from Three-dimensional Images of Cryo-Electron Microscopy". Old Dominion University, 140 pages, 2015.