All Publications


  • Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature Liu, R., Rizzo, S., Whipple, S., Pal, N., Pineda, A. L., Lu, M., Arnieri, B., Lu, Y., Capra, W., Copping, R., Zou, J. 2021

    Abstract

    There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging1-3. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety.

    View details for DOI 10.1038/s41586-021-03430-5

    View details for PubMedID 33828294

  • Modeling Spatial Correlation of Transcripts with Application to Developing Pancreas SCIENTIFIC REPORTS Liu, R., Mignardi, M., Jones, R., Enge, M., Kim, S. K., Quake, S. R., Zou, J. 2019; 9
  • Modeling Spatial Correlation of Transcripts with Application to Developing Pancreas. Scientific reports Liu, R., Mignardi, M., Jones, R., Enge, M., Kim, S. K., Quake, S. R., Zou, J. 2019; 9 (1): 5592

    Abstract

    Recently high-throughput image-based transcriptomic methods were developed and enabled researchers to spatially resolve gene expression variation at the molecular level for the first time. In this work, we develop a general analysis tool to quantitatively study the spatial correlations of gene expression in fixed tissue sections. As an illustration, we analyze the spatial distribution of single mRNA molecules measured by in situ sequencing on human fetal pancreas at three developmental time points-80, 87 and 117days post-fertilization. We develop a density profile-based method to capture the spatial relationship between gene expression and other morphological features of the tissue sample such as position of nuclei and endocrine cells of the pancreas. In addition, we build a statistical model to characterize correlations in the spatial distribution of the expression level among different genes. This model enables us to infer the inhibitory and clustering effects throughout different time points. Our analysis framework is applicable to a wide variety of spatially-resolved transcriptomic data to derive biological insights.

    View details for PubMedID 30944357

  • The Effects of Memory Replay in Reinforcement Learning Liu, R., Zou, J., IEEE IEEE. 2018: 478–85
  • Wide-Field Optical Microscopy of Microwave Fields Using Nitrogen-Vacancy Centers in Diamonds Advanced Optical Materials Shao, L., Liu, R., Zhang, M., Shneidman, A. V., Audier, X., Markham, M., Dhillon, H., Twitchen, D. J., Xiao, Y., Loncar, M. 2016; 4 (7): 1075–1080

    View details for DOI 10.1002/201600039

  • Enhanced Raman scattering of single nanoparticles in a high- Q whispering-gallery microresonator PHYSICAL REVIEW A Liu, R., Jin, W., Yu, X., Liu, Y., Xiao, Y. 2015; 91 (4)
  • Cooling mechanical resonators to the quantum ground state from room temperature PHYSICAL REVIEW A Liu, Y., Liu, R., Dong, C., Li, Y., Gong, Q., Xiao, Y. 2015; 91 (1)