Publications

* corresponding author

Journal Papers

  • Li-An Tsai, Estelle Nuckels, and Yingfeng Wang*, “Integrating Quantum Computing into De Novo Metabolite Identification,” Journal of Systemics, Cybernetics and Informatics, vol. 21, no. 2, pp. 83-86, 2023. (Also see IMCIC 2023)
  • Myungjae Kwak, Matthew Molina, Spencer Arnold, Andrew Woodward, Jin-Young An, Estelle Nuckels, and Yingfeng Wang*, “Metabolite Fragmentation Visualization,” Journal of Systemics, Cybernetics and Informatics, vol. 20, no. 5, pp. 138-147, 2022. (Also see IMCIC 2022)
  • Connie Sun, Vijayalakshmi K. Kumarasamy, Yu Liang*, Dalei Wu, and Yingfeng Wang, “Using a Layered Ensemble of Physics-Guided Graph Attention Networks to Predict COVID-19 Trends,” Applied Artificial Intelligence, vol. 36, no. 1, pp. 2055989, 2022.
  • Myungjae Kwak*, Kyungwoo Kang, and Yingfeng Wang, “Methods of Metabolite Identification Using MS/MS Data,” Journal of Computer Information Systems, vol. 62, no. 1, pp. 12-18, 2022.
  • Meng Hsiu Tsai and Yingfeng Wang*, “Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19,” International Journal of Environmental Research and Public Health, vol. 18, no. 12, pp. 6272, 2021.
  • Yingfeng Wang*, Biyun Xu, Myungjae Kwak, and Xiaoqin Zeng, “A Noise Injection Strategy for Graph Autoencoder Training,” Neural Computing and Applications, vol. 33, no. 10, pp. 4807-4814, 2021. (Also see ICMLC 2020)
  • Xiaoqin Zeng, Yufeng Liu, Zhan Shi, Yingfeng Wang, Yang Zou, Jun Kong, and Kang Zhang, “Edge-Based Graph Grammar: Theory and Support System,” Journal of Visual Languages and Sentient Systems, vol. 4, pp. 28, 2018.
  • Yingfeng Wang*, Xutao Wang, and Xiaoqin Zeng, “MIDAS-G: A Computational Platform for Investigating Fragmentation Rules of TandemMass Spectrometry in Metabolomics,” Metabolomics, vol. 13, no. 10. pp. 116, 2017.
  • Xubo Wang and Yingfeng Wang*, “Discrete Mathematics Education for Information Technology Students,” Computer Education, no. 4. pp. 16-20, 2016.
  • Yingfeng Wang, Russell L. Malmberg, and Liming Cai, “A Novel Structural Measure Separating Non-Coding RNAs from Genomic Backgrounds,” Tsinghua Science and Technology, vol. 20, no. 5. pp. 474-483, 2015.
  • Changhui Yan and Yingfeng Wang, “A Graph Kernel Method for DNA-binding Site Prediction,” BMC Systems Biology, vol. 8, no. Suppl 4:S10, 2014.
  • Yingfeng Wang, Guruprasad Kora, Benjamin Bowen, and Chongle Pan, “MIDAS: A Database-Searching Algorithm for Metabolite Identification in Metabolomics,” Analytical Chemistry, vol.86, no. 19. pp. 9496-9503, 2014.
  • Nicholas B. Justice, Zhou Li, Yingfeng Wang, Susan E. Spaudling, Annika C. Mosier, Robert L. Hettich, Chongle Pan, and Jillian Banfield, “15N- and 2H Proteomic Stable Isotope Probing Links Nitrogen Flow to Archaeal Heterotrophic Activity,” Environmental Microbiology, vol. 16, no.10, pp. 3224-3237, 2014.
  • Zhou Li, Yingfeng Wang, Qiuming Yao, Nicholas Justice, Tae-Hyuk Ahn, Dong Xu, Robert Hettich, Jillian Banfield, and Chongle Pan, “Diverse and Divergent Protein Post-translational Modifications in Two Growth Stages of A Natural Microbial Community,” Nature Communications, vol 5, no. 4405, 2014.
  • Yingfeng Wang, Tae-Hyuk Ahn, Zhou Li, and Chongle Pan, “Sipros/ProRata: A Versatile Informatics system for Quantitative Community Proteomics,” Bioinformatics, vol 29, no 16, pp 2064-2065, 2013.
  • Amirhossein Manzourolajdad, Yingfeng Wang, Timothy I. Shaw, and Russell L. Malmberg, “Information-theoretic Uncertainty of SCFG-modeled Folding Space of the Non-coding RNA,” Journal of Theoretical Biology, vol 318, pp 140-163, 2013.
  • Pooya Shareghi, YingfengWang, Russell L. Malmberg, and Liming Cai, “Simultaneous Prediction of RNA Secondary Structure and Helix Coaxial Stacking,” BMC Genomics, vol 13, Suppl 3 S7, 2012. (Also see BIBM 2011.)
  • Yingfeng Wang, Amir Manzour, Pooya Shareghi, Timothy I. Shaw, Ying-Wai Li, Russell L. Malmberg, and Liming Cai, “Stable Stem Enabled Shannon Entropies Distinguish Noncoding RNAs from Random Backgrounds,” BMC Bioinformatics, vol 13, Suppl 5 S1, 2012. (Also see ICCABS 2011.)
  • Timothy I. Shaw, Amir Manzour, Yingfeng Wang, Russell L. Malmberg, and Liming Cai, “Analyzing Modular RNA Structure Reveals Low Global Structural Entropy in MicroRNA Sequence,” Journal of Bioinformatics and Computational Biology, vol.9, no.2, pp.283-298, 2011. (Also see CSB 2010.)
  • Leilei Guo, Dong Zhang, Yingfeng Wang, Russell L. Malmberg, Michael J. McEachern, and Liming Cai, “TRFolder: Computational Prediction of Novel Telomerase RNA Structures in Yeast Genomes,” International Journal of Bioinformatics Research and Applications, vol.7, no.1, pp.63-81, 2011.
  • Yingfeng Wang, Zhibin Huang, Yong Wu, Russell L. Malmberg, and Liming Cai, “RNATOPS-W: A Web Server for RNA Structure Searches of Genomes,” Bioinformatics, vol. 25, no. 8, pp. 1080-1081, 2009.
  • Xiaoqin Zeng, Jing Shao, Yingfeng Wang, and Shuiming Zhong, “A Sensitivity-based Approach for Pruning Architecture of Madalines”, Neural Computing and Applications, vol. 18, no. 8, pp. 957-965, 2009.
  • Changhui Yan, Jing Hu, and YingfengWang, “Discrimination of Outer Membrane Proteins Using a K-nearest Neighbor Method,” Amino Acids, vol. 35, no. 1, pp. 65-73, 2008.
  • Changhui Yan, Jing Hu, and Yingfeng Wang, “Discrimination of Outer Membrane Proteins with Improved Performance,” BMC Bioinformatics, vol. 9, no. 47, 2008.
  • Yingfeng Wang, Xiaoqin Zeng, Daniel So Yeung, and Zhihang Peng, “Computation of Madalines Sensitivity to Input and Weight Perturbations,” Neural Computation, vol. 18, no. 11, pp. 2854-2877, 2006.
  • Xiaoqin Zeng, Yingfeng Wang, and Kang Zhang, ”Computation of Aadalines Sensitivity to Weight Perturbation,” IEEE Transactions on Neural Networks, vol. 17, no. 2, pp. 515-519, 2006.

Lecture Notes

  • YingfengWang, Xiaoqin Zeng, and Daniel S. Yeung, “Sensitivity Analysis of Madalines to Weight Perturbation,” Lecture Notes in Artificial Intelligence, vol. 3930, pp. 822-831, 2006. (Also see ICMLC2005)

Conference Proceedings

  • Li-An Tsai, Estelle Nuckels, and Yingfeng Wang*, “Integrating Quantum Computing into De Novo Metabolite Identification,” Proceedings of the International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC), pp. 84-87, 2023.
  • Amin Riazi and Yingfeng Wang*, “Using Topological Analysis to Investigate True and False Information Diffusion,” Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), pp. 851-855, 2022.
  • Hong Qin*, Syed Tareq, William Torres, Megan Doman, Cleo Falvey, Jamaree Moore, Meng Hsiu Tsai, Yingfeng Wang, Azad Hossain, Mengjun Xie, and Li Yang, “Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States,” Proceedings of The 18th International Conference on Data Science (ICDATA), accepted.
  • Myungjae Kwak, Matthew Molina, Spencer Arnold, Andrew Woodward, Jin-Young An, Estelle Nuckels, and Yingfeng Wang*, “FragView: An educational web server for visualizing metabolite fragmentation,” Proceedings of the International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC), pp. 120-123, 2022. (The journal version of this paper has been invited to publish in the Journal of Systemics, Cybernetics and Informatics)
  • Meng Hsiu Tsai, Nicole Marie Ely, and Yingfeng Wang*, “Uncertainty Estimation for Twitter Inference,” Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1437-1440, 2021.
  • Meng Hsiu Tsai and Yingfeng Wang*, “A New Ensemble Method for Classifying Sentiments of COVID-19-Related Tweets,” Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), pp. 313-316, 2020.
  • Yingfeng Wang, Biyun Xu, Myungjae Kwak, and Xiaoqin Zeng, “A Simple Training Strategy for Graph Autoencoder,” Proceedings of the International Conference on Machine Learning and Computing (ICMLC), pp 341-345, Feb. 2020. (The journal version of this paper is published in Neural Computing and Applications)
  • Meng-Hsiu Tsai, Yingfeng Wang*, Myungjae Kwak, and Neil Rigole, “A Machine Learning Based Strategy for Election Result Prediction,” Proceedings of International Conference on Computational Science and Computational Intelligence (CSCI), pp 1408-1410, Dec. 2019.
  • Xiaoqin Zeng, Yufeng Liu, Zhan Shi, YingfengWang, Yang Zou, Jun Kong, and Kang Zhang, “An Edge-Based Graph Grammar Formalism and Its Support System,” Proceedings of International DMS Conference on Visualization and Visual Languages (DMSVIVA), pp 101-108, Jun. 2018. (The journal version of this paper has been selected to publish in Journal of Visual Languages and Sentient Systems.)
  • Patrick Andrade and Yingfeng Wang, “A Graphical User Interface for Designing Graph Grammars,” Proceedings of Annual Conference of the Southern Association for Information Systems (SAIS), pp 9, Mar. 2018.
  • John Girard, Jennifer Breese, and Yingfeng Wang, “Hospital Technology Integration in Southern US States,” Proceedings of Annual Conference of the Southern Association for Information Systems (SAIS), paper 24, Mar. 2016.
  • Pooya Shareghi, Yingfeng Wang, Russell L. Malmberg, and Liming Cai, “Simultaneous Prediction of RNA Secondary Structure and Helix Coaxial Stacking,” Proceedings of 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.89-95, Nov. 2011. (The journal version of this paper has been selected to publish in BMC Genomics.)
  • Yingfeng Wang, Amir Manzour, Pooya Shareghi, Timothy I. Shaw, Ying-Wai Li, Russell L.Malmberg, and Liming Cai, “Stable Stem Enabled Shannon Entropies Distinguish Noncoding RNAs from Random Backgrounds,” Proceedings of 1st IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS), pp.184-189, Feb. 2011. (The journal version of this paper has been invited to publish in BMC Bioinformatics.)
  • Timothy I. Shaw, Amir Manzour, Russell L. Malmberg, Yingfeng Wang, and Liming Cai, “Analyzing Modular RNA Structure Reveals Low Global Structural Entropy in MicroRNA Sequence,” Proceedings of 9th Annual International Conference on Computational Systems Bioinformatics (CSB), pp. 146-155, Aug. 2010. (The journal version of this paper has been selected to publish in Journal of Bioinformatics and Computational Biology.)
  • Yingfeng Wang and Xiaoqin Zeng, “Using a Sensitivity Measure to Improve Training Accuracy and Convergence for Madalines,” Proceedings of International Joint Conference on Neural Networks (IJCNN), pp. 1750-1756, Jul. 2006
  • Yingfeng Wang, Xiaoqin Zeng, and Daniel S. Yeung, “Analysis of Sensitivity Behavior of Madalines,” Proceedings of IEEE International Conference on Machine Learning and Cybernetics (ICMLC), pp. 4731-4737, Aug. 2005. (This paper has been selected to publish in Lecture Notes in Artificial Intelligence.)
  • Yingfeng Wang, Xiaoqin Zeng, and Lixin Han, “Sensitivity of Madalines to Input and Weight Perturbations,” Proceedings of IEEE International Conference on Machine Learning and Cybernetics (ICMLC), pp.1349-1354, Nov. 2003.