Yiling Chen

PhD Student in Statistics (2016-2021)
Research interest: 

Yiling is a Ph.D. student in Statistics at UCLA. Her research interest involves developing and refining statistical methods to solve questions related to biology and medicine. More specifically, she is working on improving the performance of machine learning tools on medical data with high noise. She enjoys exercising.

Next Postdoctoral Researcher in Dr. Wei Li's Lab at UC Irvine, Currently Statistical Scientist at Genentech.


78. Chen, Y.E.*Ge, X.*, Woyshner, K.*, McDermott, M.*, Manousopoulou, A., Ficarro, S., Marto, J., Kexin LiWang, L.D., and Li, J.J. (2024). APIR: a universal FDR-control framework for boosting peptide identification power by aggregating multiple proteomics database search algorithms. Genomics, Proteomics & Bioinformatics (accepted). [ SOFTWARE ] [ CODE ]

65. Zhang, C., Chen, Y.E., Zhang, S., and Li, J.J. (2022). Information-theoretic classification accuracy: a criterion that guides data-driven combination of ambiguous outcome labels in multi-class classification. Journal of Machine Learning Research 23(341):1−65. [ RECOMB 2023 ] [ SOFTWARE ] | [ PDF ]

63. Say, I., Chen, Y.E., Sun, M.Z., Li, J.J., and Lu, D.C. (2022). Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation. Frontiers in Rehabilitation Sciences 3:1005168.

56. Ge, X.*, Chen, Y.E.*, Song, D., McDermott, M., Woyshner, K., Manousopoulou, A., Wang, N., Li, W., Wang, L.D., and Li, J.J. (2021). Clipper: p-value-free FDR control on high-throughput data from two conditions. Genome Biology 22:288. [ UCLA NEWS ] [ SOFTWARE ] [ CODE ] [ VIDEO ] | [ PDF ]

55. Shi, J., Xu, J., Chen, Y.E., Li, J.S., Cui, Y., Shen, L, Li, J.J., and Li, W. (2021). The concurrence of DNA methylation and demethylation is associated with transcription regulation. Nature Communications 12:5285.

49. Li, J.J.Chen, Y.E., and Tong, X. (2021). A flexible model-free prediction-based framework for feature ranking. Journal of Machine Learning Research 22(124):1–54. [ SOFTWARE ]

19. Li, W.V.Chen, Y., and Li, J.J. (2017). TROM: a testing-based method for finding transcriptomic similarity of biological samples. Statistics in Biosciences 9(1):105–136. [ SOFTWARE ]