11/21/19: Our manuscript on pancreatic cancer subtyping is published in CCR. We evaluate existing molecular subtyping methods and develop an accurate machine learning algorithm (PurIST) to predict pancreatic cancer subtype in new patients. To access PurIST, please contact us with your github ID. Media: Lineberger, Gillings SPH, ABC 11 news, PANCAN. PDF.
11/14/19: Pedro’s work on consensus peak calling in high-throughput epigenomic datasets is highlighted in the Bios Departments monthly newsletter BiosBeat.
I am an assistant professor in the Department of Biostatistics at the Gillings School of Global Public Health at UNC-CH, with a joint appointment at the Lineberger Comprehensive Cancer Center. My methodological work spans several areas in genomics and statistics, addressing problems facing basic science, translational, and clinical researchers in cancer.
Previously, I was a Postdoctoral Research Fellow with Giovanni Parmigiani in the Department of Data Sciences at the Dana-Farber Cancer Institute and the Department of Biostatistics at the Harvard T. H. Chan School of Public Health (joint with Nikhil Munshi). I earned my PhD in the Department of Biostatistics at UNC under the direction of Joseph Ibrahim and Wei Sun.
Research Interests and Activities
Precision medicine, high throughput epigenomics (ChIP-seq, ATAC-seq,etc.), multi-study learning, gene signature replicability, model-based clustering, alternative splicing (RNA-seq), proteomics, pancreatic cancer, and breast cancer. Additional detail can be found under Projects and Students.
Collaborative Work: As a member of the Lineberger Biostatistics Core I assist physicians and researchers with statistical problems relating to genomics and clinical studies. I am available for basic statistical consulting queries for all Lineberger Members.
Cancer Clinical Trials: I help oncologists design cancer clinical trials at UNC and elsewhere, serving as trial statistician on a number of active protocols. I am also a part of the Translational Breast Cancer Research Consortium Statistical Working Group, where I develop and review novel clinical trials in breast cancer with oncologists nationwide.
Teaching: I teach a course on Statistical Computing, BIOS 735, to PhD Students at Gillings, covering topics such R programming, various statistical and computational algorithms, and machine learning models.
10/14/19: Our manuscript on training cross-study replicable prediction models is published in JASA. We evaluate several common strategies in building clinical prediction models from gene expression data, and propose a novel pGLMM to select study-consistent predictors from multiple datasets. Lineberger Press Release. PDF
10/1/19: Notice of award for the HEAL Initiative BACPAC consortium grant. I will have two GRA positions available in Fall 2019 to perform multiplatform genomic data integration with multiple clinical phenotypes. SPH Release
5/13/19: Pedro Baldoni’s Manuscript on detecting consensus regions of epigenomic activity from multiple high-throughput sequencing datasets is published in Biometrics. Link
4/15/19: Pedro received the prestigious University Cancer Research Fund SPH Student Award for 2019! This $30,000 award goes towards a student’s stipend, tuition, and fees for the year and is awarded to SPH students that show strong promise in cancer research.