5/10/17 - Junior faculty mentoring sponsored topic.
5/3/17 - Meeting of the Biostatistics Student Association (BSA) of the American Statistical Association (ASA).
4/26/17 - Matthew Mulvahill, MS - Topic forthcoming.
4/19/17 - Meeting of the Colorado/Wyoming Chapter of the American Statistical Association (ASA).
4/12/17 - Larry Hunter, PhD - Pharmacology - Topic forthcoming.
4/5/17 - Chris Gignoux, MD - Biomedicine - Topic forthcoming.
3/29/17 - Damian Wandler - Oracle - Topic forthcoming.
3/22/17 - Spring Break
3/16/17 (Thursday, 12 pm) - Keith Baggerly, PhD - Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, University of Texas -"Vitamin D, Recommendations, Reproducibility, and Controversy."
The easiest way to treat disease is
often to prevent it from developing in the first place. Many prevention
strategies involve nutrition and vitamin intake; these are important
that many governments “recommend” target levels. Determining what these
levels should be involves integrating results from many types of studies
applying statistical modeling to identify intakes which will ensure most
population (97.5%) will have their biochemical requirements met. Because
recommendations can direct policy, and can wind up affecting the health
millions, their derivations should ideally be clear and checkable. This
always the case. In this talk, we’ll review how the current US
Recommended Dietary Allowance (RDA) for vitamin D, 600 International
Units (IU) per day, was
determined in a 2011 report from the Institute of Medicine (IOM). We
RDA is too low by at least a factor of 2, and more likely by a factor of
to errors in mathematical modeling (e.g., dividing by the wrong number).
then shift the discussion from mathematics to policy, and talk about how
now trying to fix the errors and what this entails.
3/16/17 (Thursday, 10 am) - Sara Selitsky - UNC Chapel Hill - Faculty candidate talk - Topic forthcoming.
3/14/17 (Tuesday, 9 am) - David Astling - University of Colorado - Faculty candidate talk - "Bioinformatic Approaches for Exploring Molecular Immune Responses."
3/8/17 - Fyong Xing, MS - University of Florida - Informatics Faculty Candidate - "High-throughput Biomedical Image Computing for Digital Health."
In biomedical informatics, a large amount of image data
has been collected to support clinical diagnosis, treatment decision and
medical prognosis. The large volume and the diversity of informatics across
different imaging modalities require advanced and high-throughput image
computing technologies to provide more accurate disease detection, deeper
understanding of the mechanisms of disease progression, and better healthcare
in precision medicine. With the ever-increasing amount of biomedical image
data, it is very important to design and develop efficient technologies
for large-scale biomedical image analysis. This talk will describe
high-throughput biomedical image computing methods for digital health, focusing
on three significant topics: object detection, segmentation, and image
understanding in medical diagnosis. Specifically, I will present several novel
machine learning and imaging informatics technologies to process biomedical big
image data and the applications in medical diagnosis.
3/1/17 - Ayush Goyal, PhD - Texas A&M University - Informatics Faculty Candidate - "Multi-scale Filtering and Fuzzy Clustering for
Segmentation and 3-D Reconstruction from Medical Images."
advances in biomedical image processing have been applied to three-dimensional
volumetric medical image data for reconstructing 3-D computational models
of organs and vasculature. Specifically, this talk will present automatic
image processing algorithms based on multi-scale filtering and fuzzy clustering
applied on optical fluorescence microtomography and brain and cardiac MRI
/ CT images for reconstruction of whole-organ or patient-specific
computational models of brain gray and white matter, cardiac geometry, coronary
or cerebral vasculature. Additionally, pixel-clustering methods, such as
adapted fuzzy c-means or k-means modified and combined with connected region
labeling, used to extract and reconstruct the brain gray and white matter
regions and cardiac left and right ventricles will be discussed for both
calculation of brain gray and white matter volume and assessment of a patient’s
cardiac left ventricle and myocardial wall with computation of clinical
parameters such as ejection fraction, stroke volume, and end-systolic and
end-diastolic volume. A side application - the same image processing methods of
pixel clustering and multi-scale line filtering can also be applied for
segmentation of other objects of interest such as cells or bacteria in
microscopic and histological images. We will discuss applying this approach to
automatic disease screening by detection of pathological agents such as
mycobacterium tuberculosis in stained microscopic or histological smears of
patients (sputum, blood, or urine).
2/15/17 - Laura Wiley, PhD - Division of Biomedical Informatics and Personalized Medicine - "Precision Medicine and the Learning Healthcare System: Leveraging Informatics to Improve Care."
Precision medicine and the learning healthcare system are predicated
on the assumption that by turning data (be they molecular, clinical, or
social) into knowledge we can improve health. Informatics is a key
enabler of this vision. Using warfarin pharmacogenomics as an example,
we will examine the role of informatics for data discovery, translation,
implementation and evaluation.
2/1/17 - Journal Club - "Calling BS in the Age of Big Data".
Discussion of the syllabus for a new course at University of
Washington. The aim of this course is to teach people how to think
critically about the data and models that constitute evidence in the
social and natural sciences.
2/8/17 - Emanuele Giorgi - University of Lancaster - "Disease mapping and visualization using data from spatio-temporally referenced prevalence surveys."
We set out general principles and develop statistical tools for the
analysis of data from spatio-temporally referenced prevalence surveys.
Our objective is to provide a tutorial guide that can be used in order
to identify parsimonious geostatistical models for prevalence mapping. A
general variogram-based Monte Carlo procedure is proposed to check the
validity of the modelling assumptions. We describe and contrast
likelihood-based and Bayesian methods of inference, showing how to
account for parameter uncertainty under each of the two paradigms. We
also describe extensions of the standard model for disease prevalence
that can be used when stationarity of the spatio-temporal covariance
function is not supported by the data. We discuss how to define
predictive targets and argue that exceedance probabilities provide one
of the most effective ways to convey uncertainty in prevalence
estimates. We describe statistical software for the visualization of
spatio-temporal predictive summaries of prevalence through interactive
animations. Finally, we illustrate an application to historical malaria
prevalence data from 1334 surveys conducted in Senegal between 1905 and
2016 Biostatistics and Informatics Seminars
2014-2015 Biostatistics and Informatics Seminars and Journal Club