Publication

Manuscript

Liang, M.*, Ning, Y, Smith, M., and Zhao, Y.Q. Inference with non-differentiable surrogate loss in a general high-dimensional classification framework. Major revision.
Liang, M.*, Zhao, Y.Q., Lin, D., Cooperberg, M., and Zheng, Y. Estimating optimally tailored active surveillance strategy under interval censoring. Major revision.
Liang, M.*, Ye, T., Zhao, Y.Q. A General Framework for Incorporating Identification Uncertainty in Individualized Treatment Rules. R&R in JRSSB.
Liang, M.*, Wu, R., Yang. S, Guo, Y., Zhao, Y.Q. Classification under Outcome Misclassification: Reliability Quantification and Partial Identification. Under review.
Bhattacharya, S.*, Chen, Y. (student), Liang, M.* (Alphabet-order) Multi-task Learning for Semiparametric Models: Late Fusion and Nuisance Estimation. Under review.
Ying, C., Jin, J., Guo, Y., Li, X., Liang, M., Zhao, J. Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift. Submitted to ICML.
Li, Z. (student), Diaz-Rincon, R., Shickel, B., Zhang, S., Bhattacharya, S., Liang, M.* Classification-Powered Conformal Inference for Zero-inflated Outcomes with an Application to Dose Change Prediction. Submitted to ICML.

Methodology

Park, J. (student), Liang, M.*, Zhao, Y. Q., and Zhong, X. (2024). Eﬀicient surrogate-assisted inference for patient-reported outcome measures with complex missing mechanism. Electronic Journal of Statistics, Accepted.
Liang, M.*, Park, J. (student), Lu, Q., and Zhong, X. (2024). Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes. Biometrics, Accepted.
Hou, T., Shen, X., Zhang, S., Liang, M., Lu Q. (2024). AIGen: An Artificial Intelligence Software for Complex Genetic Data Analysis. Briefings in Bioinformatics, 25(6).
Liang, M., and Yu, M. (2023). Relative contrast estimation and inference for treatment recommendation. Biometrics, 4(79), 2920-2932.
Liang, M., Choi, Y.G., Ning, Y, Smith, M., and Zhao, Y.Q. (2022). Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score. Journal of Machine Learning Research, 23(262), 1-65.
Liang, M., and Yu, M. (2022). A semiparametric approach to model effect modification. Journal of the American Statistical Association, 117(538), 752-764.
Li, Y., Liang, M., Mao, L., and Wang, S. (2021). Robust estimation and variable selection for the accelerated failure time model. Statistics in Medicine, 40(20), 4473- 4491.
Liang, M., and Zhao, Y.Q. (2021). Comment on “More eﬀicient policy learning via opti- mal retargeting” and “Learning optimal distributionally robust individualized treatment rules”. Journal of the American Statistical Association, 116 (534), 690-693.
Liang, M.*, Ye, T., Fu H. (2018). Estimating individualized optimal combination therapies through outcome-weighted deep learning algorithms. Statistics in Medicine, 37(27), 3869-3886.
Huling, J. D., Yu, M., Liang, M., Smith, M. (2018). Risk prediction for heterogeneous populations with application to hospital admission prediction. Biometrics, 74(2), 557- 565.
Zhang, S., Liang, M., Zhou, Z., Zhang, C., Chen, N., Chen, T., and Zeng, J. (2017). Elastic restricted boltzmann machines for cancer data analysis. Quantitative Biology, 5(2), 159-172.
Liang, M., Li, Z., Chen, T., and Zeng, J. (2014). Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(4), 928-937.

Collaborative Research

Guo, S., Park, J., Liang, M., Zhong, X. (2024). Investigation of the Causal Relationship between Patient Portal Utilization and Patient's Self-Care Self-Efficacy and Satisfaction in Care among Patients with Cancer. BMC Medical Informatics and Decision Making.
Balch, J., Ruppert, M., Guan, Z., Buchanan, T., Abbott, K. Shicel, B., Bihorac, A., Liang, M., Tignanelli, C., Loftus, T. (2024). Risk-Specific Training Cohorts Improve Performance of a Deep Learning Surgical Risk Prediction Model. JAMA Surgery, Dec 1;159(12):1424-1431.
Ungaro, R.F., Xu J., Kucaba T.A., Rao M., Bergmann C.B., Brakenridge S.C., Efron P.A., Goodman M.D., Gould R.W., Hotchkiss R.S., Liang M. (2024) Development and optimization of a diluted whole blood ELISpot assay to test immune function. Journal of Immunological Methods, Oct 1;533:113743.
Polcz, V., Barrios, E., Cox, M., Rocha, I., Liang, M., Hawkins, R., Darden, D., Ungaro, R., Dirain, M., Mankowski, R., Mohr, A., Moore, F., Moldawer, L. Efron, P., Brakenridge, S., Loftus, T. (2024). Severe Trauma Leads to Sustained Muscle Loss, Induced Frailty and Distinct Temporal Changes in Myokine and Chemokine Profiles of Older Patients. Surgery, 176 (5), 1516-1524
SPIES Consortium. (2024). Surviving Septic Patients Endotyped With a Functional Assay Demonstrate Active Immune Responses. Frontiers in immunology 15, 1418613.
SPIES Consortium (2024). Adverse outcomes and an immunosuppressed endotype in septic patients with reduced IFN-γ ELISpot. JCI insight, 9(2).
Imran, M., Nguyen, B., Pensa, J., Falzarano, S.M., Sisk, A.E., Liang, M., DiBianco, J.M., Su, L.M., Zhou, Y., Brisbane, W.G. and Shao, W.. (2024) Image Registration of In Vivo Micro-Ultrasound and Ex Vivo Pseudo-Whole Mount Histopathology Images of the Prostate: A Proof-of-Concept Study. Biomedical Signal Processing and Control, 96, 106657.
Jiang, H., Imran, M., Muralidharan, P., Patel, A., Pensa, J., Liang, M., Benidir, T., Grajo, J.R., Joseph, J.P., Terry, R. and DiBianco, J.M. (2024). MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images. Computerized Medical Imaging and Graphics, 112.
Yang, D., Wheeler, M., Karanth, S.D., Aduse‐Poku, L., Leeuwenburgh, C., Anton, S., Guo, Y., Bian, J., Liang, M., Yoon, H.S. and Akinyemiju, T. (2023). Allostatic load and risk of all‐cause, cancer‐specific, and cardiovascular mortality in older cancer survivors: An analysis of the National Health and Nutrition Examination Survey 1999–2010. Aging and Cancer.
Mafee, M., Buhalog, B., Liang, M., Yu, M., Aylward, J., and Xu, Y. (2023). Length-to-width ratio in Mohs defects: what is the golden rule? Archives of Dermatological Research.
Zhang, D., Spiropoulos, K. A., Wijayabahu, A., Christou, D. D., Karanth, S. D., Anton, S. D., Liang, M., ... and Braithwaite, D. (2023). Low muscle mass is associated with a higher risk of all–cause and cardiovascular disease–specific mortality in cancer survivors. Nutrition, 107, 111934.
Wang, Z., Receveur, J. P., Pu, J., Cong, H., Richards, C., Liang, M.., and Chung, H. (2022). Desiccation resistance differences in Drosophila species can be largely explained by variations in cuticular hydrocarbons. Elife 11, e80859.
Vardar, B., Meram, E., Karaoglu, K., Liang, M., Yu, M., Laeseke, P., and Ozkan, O. (2022). Radioembolization followed by Transarterial Chemoembolozation in Hepatocellu- lar Carcinoma. Cureus, 14 (4).
Park, J., Liang, M., Alpert, J. M., Brown, R. F., and Zhong, X. (2021). The causal relationship between portal usage and self-eﬀicacious health information–seeking behav- iors: secondary analysis of the health information national trends survey data. Journal of Medical Internet Research, 23(1), e17782.
Zhong, X., Park, J., Liang, M., Shi, F., Budd, P. R., Sprague, J. L., and Dewar, M. A. (2020). Characteristics of patients using different patient portal functions and the impact on primary care service utilization and appointment adherence: retrospective observa- tional study. Journal of Medical Internet Research, 22(2), e14410.
Zhong, X., Liang, M., Sanchez, R., Yu, M., Budd, P. R., Sprague, J. L., and Dewar, M. A. (2018). On the effect of electronic patient portal on primary care utilization and appointment adherence. BMC Medical Informatics and Decision Making, 18(1), 1-12.
Coriano, C.G., Liu, F., Sievers, C.K., Liang, M., Wang, Y., Lim, Y., Yu, M. and Xu, W. (2018). A computational-based approach to identify estrogen receptor α/β heterodimer selective ligands. Molecular Pharmacology, 93(3), 197-207.

Book Chapter

Liang, M., and Zhao, Y.Q. Estimation and inference of individualized treatment rules using eﬀicient augmentation and relaxation learning. Precision Medicines: Methods and Applications, Springer.

* indicates the corresponding author