News & Media
Recent Lab News
November 20, 2025
Congratulations to Wenyi and Judy!
Congratulations to professors Wenyi Wang and Huixia Judy Wang (Rice University) for receiving a Cancer Bioengineering Collaborative Seed Grant to advance conformal inference methods for predicting immunotherapy benefit.
November 15, 2025
We are now recruiting postbaccs!
The Postbacc-QB program at MD Anderson is now accepting applications for its next cohort of trainees interested in quantitative biology and cancer research. The program is directed by Dr. Wenyi Wang, who leads efforts to mentor and develop future scientists through immersive, hands-on research training. Email Dr. Wang for more information.
August 14, 2025
Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent Event Model with Competing Outcomes has been accepted at Annals of Applied Statistics (AOAS)!
This work introduces a Bayesian semi-parametric recurrent event model with competing outcomes, enabling covariate-adjusted age-to-onset penetrance curves and delivering strong predictive performance for second primary cancers (AUC: Lung ≈ 0.89, Sarcoma ≈ 0.91, Breast ≈ 0.76). These findings move us closer to personalized healthcare for cancer survivors by refining risk prediction and supporting clinical decision-making. View a preprint here.
July 31, 2025
Congratulations to Dr. Montierth!
We are delighted to congratulate Matthew Montierth on the successful defense of his Ph.D. dissertation and the completion of his doctoral journey. May this achievement be the beginning of many more accomplishments ahead!
July 22, 2025
Congratulations to Hao Yan!
Our Ph.D. student Hao Yan has been awarded the Dr. M. Stewart West Memorial Scholarship in Biometry for 2025–2026. We look forward to his continued growth and accomplishments in his academic journey!
June 19, 2025
Congratulations to Dr. Guo!
Congratulations to Shuai Guo for successfully defending his Ph.D. thesis and earning his doctorate! Wishing him all the best in his future endeavors!
May 4, 2025
Xiaoqian's transfer learning study on TP53 Wins IMS FSML Travel Award!
We’re excited to announce that our work, "Transfer Learning for Survival-based Clustering of Predictors with an Application to TP53 Mutation Annotation," has won the IMS Frontiers of Statistical Machine Learning (FSML) Travel Award after a competitive review process. This award recognizes our innovative approach in combining transfer learning and survival analysis, with potential applications in related cancer research. We look forward to presenting our work at the FSML workshop in August 2025.
April 30, 2025
Wang Lab at AACR 2025!
This April, the Wang Lab participated in the AACR 2025 conference, where we had the opportunity to present 6 posters showcasing our recent research. Congratulations to lab member and undergraduate researcher Aaron Wu’s poster on CliPP-on-Web being featured by MD Anderson’s LinkedIn page.
April 25, 2025
Dr. Wenyi Wang to present at RECOMB-CCB 2025 as a keynote speaker!
Wenyi was invited to present the groundbreaking work from our latest research at RECOMB-CCB 2025, a leading conference in computational cancer biology. Her keynote, 'Deciphering Tumor Heterogeneity for Benefits from Immunotherapy in Cancer,' provided new perspectives and contributed to ongoing discussions in the field.
January 22, 2025
Our immune profiling paper is published in the prestigious JASA!
Our collaborative work, "Immune Profiling Among Colorectal Cancer Subtypes Using Dependent Mixture Models," is now published in the Journal of the American Statistical Association. In this study, we developed a novel Bayesian modeling approach to compare T cell subtypes between early- and late-onset colorectal cancer. The model identifies immune cell populations that are condition-specific or shared, helping uncover mechanisms linked to tumor progression and potential treatment strategies. Congratulations to Yunshan and Shuai!
January 22, 2025
DeMixSC paper is published at Genome Research!
DeMixSC is an innovative deconvolution approach that overcomes the technological discrepancy between bulk and sc/snRNA-seq data using an improved wNNLS framework. It achieves high accuracy and generalizability, requiring only a small tissue-matched benchmark dataset for the targeted large bulk cohorts.
December 2024
Exciting Updates from Stats Up AI
We’re thrilled to share two major milestones for Stats Up AI: 1. ASA Approval: The Stats Up AI interest group has been officially approved by the American Statistical Association, recognizing its mission to foster collaboration and innovation at the intersection of statistics and AI. 2. Harvard Data Science Review Publication: Our recent event, Stats and AI: A Fireside Conversation, was a great success. A summary of the discussion has been accepted for publication in the Harvard Data Science Review, further amplifying its impact. These achievements highlight the growing influence of Stats Up AI in advancing the integration of statistics and AI. Stay tuned for future updates as we continue to engage with this inspiring community!
November 16, 2024
Congratulations to Carissa for winning the ABRCMS Presentation Award!
Congratulations to our summer intern, Carissa Fong, for winning the presentation award at ABRCMS (Annual Biomedical Research Conference for Minoritized Scientists)! Her award-winning presentation showcased machine learning approaches to effectively predict tumor-specific mRNA expression (TmS). We are so proud of her achievement!
May 3, 2024
Wang Lab Postdoc Ankita Paul Awarded MD Anderson IDSO Fellowship!
Congratulations to our postdoc Ankita, who has been awarded the MD Anderson Institute for Data Science in Oncology (IDSO) Fellowship! This fellowship is a great opportunity that provides junior researchers with advanced training in applying data science to oncology. We are excited to see the impactful contributions she will make through this program!
May 3, 2024
Wenyi Hooded Ph.D. Graduates Nam and Yunjie(Jeffery) at Rice Univerisity Commencement!
Congratuations again to Dr. Nguyen and Dr. Jiang. Wishing you all the best, and may your future endeavors be filled with success and fulfillment!
April 8, 2024
MuSE2.0 paper is online at Genome Research!
We are exctied to officially introduce MuSE2.0, which reduces computing time by up to 50x compared to MuSE 1.0 and 8-80x compared to other popular callers. Our benchmark study suggests combining MuSE2.0 and the recently accelerated Strelka2 achieves high efficiency and accuracy in analyzing large cancer genomic datasets.
April 3, 2024
Journal of Clinical Oncology paper is online!
We conducted a validation study of our LFSPRO software suite, which was developed for risk predictions in families with Li-Fraumeni syndrome, on a clinical patient cohort collected as part of the Clinical Cancer Genetics program at MD Anderson. Unlike research datasets that are meticulously collected over decades for research purposes, our unique dataset closely resembles what genetic counselors observe in real counseling sessions. The validation results indicate that our risk prediction models have the potential to assist decision making in clinical settings, and further highlight the importance of such validation in bridging the gap between methodology research labs and clinics.
March 27, 2024
Congratulations to Dr. Nguyen and Dr. Jiang!
Congratulations to Nam H. Nguyen and Yujie Jiang for successfully defending their Ph.D. theses and earning their doctorates!
February 2024
STATS UP AI: A community for Statistics and Biostatistics
Professors from multiple universities initiate StatsUpAI, aiming to elevate the role of statisticians in AI research. This movement emphasizes empowering statisticians to lead and innovate in addressing real-world challenges through AI research. As one of the founding members, Wenyi's involvement highlights her commitment to advancing statistical methodologies in the era of artificial intelligence. To learn more about StatsUpAI, visit their webpage.
February 12, 2024
LFSPROShiny paper is published online!
LFSPROShiny is an interactive R/Shiny application designed to perform risk prediction and visualization for Li-Fraumeni syndrome (LFS), a genetic disorder associated with TP53 mutations, enabling genetic counselors to assess patient risk profiles and support informed decision-making without the need for programming expertise.
November 9, 2023
Congratulations to the launch of Institute for Data Science in Oncology (IDSO) at MD Anderson!
IDSO integrates the most advanced computational and data science approaches with the institution’s extensive scientific and clinical expertise, aiming to profoundly enhance patient lives by revolutionizing oncological care and research. Dr. Wang's lab is proudly affiliated with this pioneering initiative, dedicated to advancements in cancer care and research through innovative methodologies.
MD Anderson Media
MD Anderson Research Highlights for May 21, 2024
Novel algorithm achieves breakthrough speed in identifying genetic alterations in tumors
MD Anderson Research Highlights for April 12, 2024
Novel model uses clinical data to predict cancer risk for Li-Fraumeni syndrome
MD Anderson’s Institute for Data Science in Oncology announces appointment of inaugural IDSO Affiliates
MD Anderson's Institute for Data Science in Oncology (IDSO) announced the appointment of its inaugural cohort of IDSO Affiliates. These 33 talented scientists, clinicians and staff, including Wenyi Wang, Ph.D., bring diverse expertise to help IDSO leadership and focus area co-leads advance collaborative data science projects and align the institute’s efforts with MD Anderson’s mission to end cancer.
Estimating tumor-specific total mRNA level predicts cancer outcomes
Researchers at MD Anderson have developed a new approach to quantify tumor-specific total mRNA levels from patient tumor samples, which contain both cancer and non-cancer cells. Using this technique on tumors from more than 6,500 patients across 15 cancer types, the researchers demonstrated that higher mRNA levels in cancer cells were associated with reduced patient survival.
Genetic diversity within tumors suggests continuous evolution
By analyzing tumors from more than 2,600 patients and from 38 cancer types, researchers from MD Anderson and fellow member institutions of the international Pan-Cancer Analysis of Whole Genomes Consortium have characterized the extensive genetic diversity across cancer and within individual tumors.