See the Campus Event Calendar for details about upcoming seminars
Location: UW Biotechnology Center Auditorium
Day and Time: Fridays from noon to 1 pm central time
Zoom options will be available, although in-person attendance is preferred.
Further details should be available about a week before the seminar.
Note: some events may have a different date and time.
To subscribe to the BMI Seminar mailing list email join-biostat-seminar@lists.wisc.edu.
Upcoming Fall 2024 Seminars
December 5, 2024: Biomedical Data Science Student Fall 2024 Rotation Presentations
Zoom: https://uwmadison.zoom.us/j/94802702215
Location Change: 408 Service Memorial Institute (SMI)
Poster: Fall 2024 Rotation Presentations
Speakers and Titles:
- James Haddad – Utilizing clinical data to predict hepatic deterioration over time
- Rotation Mentor – Matthew Churpek
- Shan Leng – Epigenetic Aging: Literature review and unsupervised solution
- Rotation Mentor: Qiongshi Lu
- Mike Fromandi – Exploring shared information in biomedical data
- Rotation Mentors: JP Yu and Daniel Pimentel-Alarcon
- JJ Liu – CASSIA
- Rotation Mentor: Christina Kendziorski
- Xiaoxu Rong – Diagnosis of neonatal encephalopathy (NE) based on CTG data
- Rotation Mentor: Daniel Pimentel-Alarcon
December 6, 2024: Biomedical Data Science Student Fall 2024 Rotation Presentations
Zoom: https://uwmadison.zoom.us/j/94802702215
Location: UW Biotechnology Center Building
Poster:
Zoom: https://uwmadison.zoom.us/j/94802702215
Poster: Fall 2024 Rotation Presentations
Speakers and Titles:
- Leo Jin – Some investigations on the sequence-to-function model Enformer
- Rotation Mentor: Qiongshi Lu
- Swathisri Venkatesh – Examining the gene regulatory programs of compensatory renal growth
- Rotation Mentor: Sushmita Roy
- Brendan Joyce – Unpacking the impact of pediatric microbiome on immune system development
- Rotation Mentor: Irene Ong
- Jin Mu – Transfer learning and genetics-powered estimation of heterogeneous treatment effect
- Rotation Mentor: Qiongshi Lu
- Peng Wu – A clinically intuitive approach to evaluating the performance of early warning scores
- Rotation Mentor: Anoop Mayampurath
- Livvy Johnson – Survival Analysis of VEGF/VEGFR inhibitor treatment on TP53-mutant cancer patients.
- Rotation Mentor: Irene Ong
Completed Seminars
September 6, 2024:
Speaker: Moo Chung, Dept of Biostatistics & Medical Informatics, UW-Madison
Title: Aligning Asynchronous Network Data through Persistent Homology
Abstract: We introduce a novel topological data analysis (TDA) approach for aligning asynchronous dynamic networks over time. Our method leverages persistent homology, which decomposes 0D topological features (connected components) and 1D topological features (loops) orthogonally. This decomposition enables the exact computation of the Wasserstein distance, a probabilistic version of optimal transport, into a squared Euclidean distance form with O(n log n) run time. Our scalable approach allows for localized matching of networks at the edge level, facilitating precise inference and learning. This method can reduce statistical variability by up to 500 times, enabling the detection of signals previously undetectable. We demonstrate the application of this method in aligning asynchronous human functional brain networks obtained from resting-state functional magnetic resonance imaging (rs-fMRI). Human brain activity at rest does not synchronize across subjects, making direct comparisons nearly impossible and posing a significant challenge to the clinical relevance of rs-fMRI. Our approach addresses this challenge by providing a workable solution that performs topological registration of time-varying networks. This talk is partially based on arXiv:2012.00675 (Annals of Applied Statistics) and arXiv:2201.00087 (PLOS Computational Biology).
September 13, 2024: Biomedical Data Science Student Summer 2024 Rotation Presentations
John Peters, BDS PhD student
- Title: Forging METL Stronger: Model User Experience
- Rotation Mentor: Professor Tony Gitter
Aurod Ounsinegad, BDS PhD student
- Title: Nonnegative Matrix Factorization Through Cone Collapsing
- Rotation Mentor: Professor Daniel Pimental Alarcon
Zhongxuan Sun, BDS PhD student
- Title: Causal regulatory network inference from Perturb-seq data
- Rotation Mentor: Professor Hyunseung Kang
Livvy Johnson, BDS PhD student
- Title: Predicting Gene Regulatory Networks for Leukemia Cell Clusters
- Rotation Mentor: Professor Sushmita Roy
Leo Jin, BDS MS student
- Title: A graph-based learning approach to predict the effects of gene perturbations on molecular phenotypes
- Rotation Mentor: Professor Mark Craven
September 20, 2024
Speaker: Will Rosenberger, George Mason University
Title: Casual Inference for Clinical Trails*: A Spellchecker’s Guide to Randomization Tests in Complex Settings
Abstract:
Sir Austin Bradford Hill, the developer of the first randomized clinical trial, was a proponent of simplicity in statistical analysis, and strongly emphasized careful study design as the critical component of all medical studies. While he didn’t mention randomization tests in his 1937 book, I believe he would have liked their simplicity and interpretability. Any inference procedure which assumes random sampling from a population ignores Fisherian principles regarding the analysis of designed experiments. And clinical trials are the quintessential designed experiment. While we hear quite often about preservation of type I error rates and, more recently, about causal inference, these are natural elements of a randomization test. We discuss these issues and demonstrate that randomization tests can be used for more complex settings, such as multiple (>2) treatment comparisons, analyses with missing outcome data, and subgroup analyses. It is interesting to note that the only cohort of statisticians NOT excited about randomization tests in this age of causal inference are the designers and conductors of randomized clinical trials! I will conclude with a few historical notes about Fisher and de Finetti.
*The two most often misspelled words during my term as Biometrics co-editor.
September 27, 2024 – CANCELLED
Speaker: Anuj Srivastava, Florida State University
Title: Statistical Shape Analysis of Complex Natural Structures
Zoom: https://uwmadison.zoom.us/j/9761550901
Abstract: Statistical modeling and analysis of structured data is a fast-growing field in Statistics and Data Science. Rapid advances in imaging techniques have led to tremendous amounts of data for analyzing imaged objects across several scientific disciplines. Examples include shapes of cancer cells, botanical trees, human biometrics, 3D genome, brain anatomical structures, crowd videos, nano-manufacturing, and so on. Shapes are relevant even in non-imaging data contexts, e.g., the shapes of COVID rate curves or the shapes of activity cycles in lifestyle data. Imposing statistical models and inferences on shapes seems daunting because the shape is an abstract notion and one requires precise mathematical representations to quantify shapes.
This talk has two parts. In the first part, I will present some recent developments in “elastic representations” of structures such as functions, curves, surfaces, and graphs. In the second part, I will focus on statistical analyses: computing shape summaries, estimation under shape constraints, hypothesis testing, time-series models, and regression models involving shapes.
October 4, 2024:
NOTE – LOCATION CHANGE – 1248 HSLC
Speaker: Daniel Pimentel-Alarcon, Dept of Biostatistics & Medical Informatics, UW-Madison
Title: Unsupervised Learning from Messy Data
Zoom: https://uwmadison.zoom.us/j/97615509019
Abstract: In this talk I will discuss some of the main challenges posed by small sample sizes, missing values, outliers, skewed groups, and sparsity patterns in Machine Learning, specifically in the unsupervised and semi-supervised setting. Most of these issues arise in modern applications of science, ranging from meta genomics to astronomy. I will also share some recent discoveries and strategies to mitigate these issues and a glimpse of some theoretical developments that might pave the road to the much needed and coveted understanding of deep learning.
October 11, 2024
Speaker: Ava Amini, Microsoft
Title: Bridging Biophysics and AI to Optimize Protein Design
Virtual Only
Zoom: https://uwmadison.zoom.us/j/97615509019
Abstract:
Engineered proteins play increasingly essential roles in applications spanning pharmaceuticals, molecular tools, synthetic biology, and more. Deep generative models offer the ability to accelerate protein engineering for therapeutic and biological applications. Recently, a family of generative models called diffusion models has demonstrated the potential for unprecedented capability and control in de novo design. In this talk, we introduce biologically-grounded diffusion models for generation of protein structures and sequences.
We first share work in creating a new diffusion-based generative model that designs protein structures by mirroring the biophysics of the native protein folding process. To expand beyond the subset of protein biology captured in structural data, we reasoned that sequence – not structure – could serve as a universal design space for protein generation. We thus developed a general-purpose diffusion framework, EvoDiff, that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models for controllable protein design in sequence space alone. We envision that these modeling frameworks will enable new capabilities in protein engineering towards programmable, functional design.
October 18, 2024:
Speaker: Yuan Ji, University of Chicago
Title: A New Nonparametrics Bayesian Models for Grouped Data with Applications to Clinical Trials Borrowing External Data
Zoom: https://uwmadison.zoom.us/j/97615509019
Abstract: We consider a class of nonparametric Bayesian models called the Shared Atoms Model (SAM) based on the dependent Dirichlet processes. SAM uses a simple idea of atom skipping to generate group specific patterns of clustering, allow atoms to be common, unique, and shared across multiple groups. In finite data sets, SAM clusters experimental units in a similar fashion, facilitating interpretable and flexible inference. We consider an application of SAM to a real-world data set with measurements of patients of atopic dermatitis.
October 25, 2024
Speaker: Ting Ye, University of Washington
Title: From Estimands to Robust Interference of Treatment Effect in Platform Trials
Zoom: https://uwmadison.zoom.us/j/97615509019
Abstract: A platform trial is an innovative clinical trial design that uses a master protocol (i.e., one overarching protocol) to evaluate multiple treatments in an ongoing manner and can accelerate the evaluation of new treatments. However, the flexibility that marks the potential of platform trials also creates inferential challenges. Two fundamental challenges are the precise definition of treatment effects and the robust and efficient inference on these effects. In this work, we make a key contribution by, for the first time, clearly stating how to construct a clinically meaningful estimand. This estimand characterizes the treatment effect as a contrast of the expected outcomes between two treatments in a population of concurrently eligible participants—the largest population that preserves the integrity of randomization. Then, we develop weighting and post-stratification methods for estimation of treatment effects with minimal assumptions. To fully leverage the efficiency potential of data from concurrently eligible participants, we also consider a model-assisted approach for baseline covariate adjustment to gain efficiency while maintaining robustness against model misspecification. We derive and compare asymptotic distributions of proposed estimators in theory and propose robust variance estimators. The proposed estimators are empirically evaluated in a simulation study and illustrated using the SIMPLIFY trial.
November 1, 2024
Speaker: Hannah Wayment-Steele, Visiting Assistant Professor, Dept of Biochemistry
Title: Predicting and Discovering Protein Dynamics
Zoom: https://uwmadison.zoom.us/j/97615509019
Abstract: The functions of biomolecules are often based in their ability to convert between multiple conformations. Recent advances in deep learning for predicting and designing single structures of proteins mean that the next frontier lies in how well we can characterize, model, and predict protein dynamics. In the first part of my talk, I will describe a simple adaptation of AlphaFold to predict multiple conformations. Combining the resulting “AFCluster” method and NMR dynamics experiments allowed us to learn more about the complete conformational landscape of KaiB, and how the slow interconversion that biology necessitates for circadian rhythms is encoded in its sequence. However, a major bottleneck for the field of predicting dynamics has been a lack of standardized datasets of experimental measurements of the timescales of protein motions, and especially those on a micro-millisecond timescale where many biologically-relevant processes occur. In the second part of my talk, I will describe the development of large-scale benchmarks of dynamics from across multiple types of NMR experiments, and initial insights if it might already be possible to predict the presence of biologically-relevant motions.
November 7, 2024: DeMets Lectures: Janet Wittes, Wittes LLS
November 8, 2024: DeMets Lectures: Janet Wittes, Wittes LLS
November 15, 2024:
Speaker: Yaping Liu, Northwestern University
Title: Decoding the human genome by multi-omics in cell-free DNA and single-cells
Zoom: https://uwmadison.zoom.us/j/97615509019
Abstract: Epigenetic modifications, including DNA methylation, histone modifications, and three-dimensional (3D) genome topology, combine with genetic content to determine the mammalian transcriptional factor (TF) binding and, thus, gene regulation. At present, we are limited by the number of simultaneous measurements that we can perform in the same DNA molecules and single cells. We developed single-cell Methyl-HiC to reveal the heterogeneity of DNA methylation, long-range DNA methylation concordance, and 3D genome in the same cells.
Recently, we improved this technology to jointly profile genetic variants, DNA methylation, chromatin accessibility, and 3D genome in the same DNA molecules and in single cells at both cell lines and flash-frozen tissues.
To non-invasively monitor the dynamics of regulatory elements in vivo, we developed a set of computational methods to study the cellular epigenomes from cell-free DNA (cfDNA) fragmentation patterns. Specifically, we developed a computational method to de novo characterize the genome-wide cfDNA fragmentation hotspots, infer the open chromatin regions within cells, and boost the power for the cancer early detection. We also developed a computational model to accurately predict DNA methylation and identify the tissues-of-origin in cfDNA from both high-coverage and low-coverage cfDNA whole-genome sequencing.
The experimental approaches in single-cell multi-omics and computational methods in cell-free DNA epigenomics developed in our lab will eventually pave the road for our understanding of the variation of cis-regulatory elements non-invasively across different physiological and pathological conditions.
November 22, 2024:
Speaker: Jennifer Clark Nelson (Kaiser Permanente Washington Health Research Institute
Title: Statistical methods for improving post-licensure vaccine safety surveillance.
Zoom: https://uwmadison.zoom.us/j/97615509019
Abstract: Improving statistical methods for post-licensure vaccine safety surveillance is critical for safeguarding public health and maintaining public trust in vaccination programs. This is especially important during pandemics like COVID-19 when vaccines are administered on a global scale at unprecedented speed. Many national vaccine safety surveillance efforts use electronic health records and insurance claims data from large multi-site health care data networks. I will summarize challenges that can arise when using these secondary data sources to conduct safety studies. I will also discuss statistical approaches designed to better detect rare adverse events in these settings. These include 1) adapting sequential methods from clinical trials to this observational database setting in order to ensure more rapid detection and 2) using natural language processing of clinical notes in combination with machine learning methods to improve the accuracy with which vaccine safety outcomes are identified. I will illustrate methods using example safety questions that have arisen within FDA’s Sentinel Initiative and the CDC’s Vaccine Safety Datalink monitoring systems.
November 29, 2024: No seminar – Thanksgiving holiday