Welcome to the Institute for Biomedical Informatics at the University of Kentucky. The Institute facilitates data-intensive, multidisciplinary team science to improve the health of patients and populations, in Kentucky and beyond.

News

UK Researchers Awarded $1.1 Million Grant to Develop Metabolomics Data Analysis Tools

Aug 26, 2020

The National Science Foundation recently awarded a three-year, $1,163,869 grant to the University of Kentucky to develop new state-of-the-art metabolomics data analysis tools that will derive new data, knowledge and interpretation from the active metabolic state of organisms and ecosystems with broad biological and biomedical applications.

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UK Joins National Data Collaborative for COVID-19 Research

Jul 08, 2020

The University of Kentucky's Center for Clinical and Translational Science (CCTS) is partnering with the National Center for Advancing Translational Science (NCATS), the National Center for Data to Health, and around 60 other clinical institutions affiliated with the NCATS Clinical and Translational Science Awards Program to leverage big data in the fight against COVID-19.

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Talbert Selected as Director of Institute for Biomedical Informatics

May 13, 2020

Jeffery Talbert, PhD, has been selected as director of the UK Institute for Biomedical Informatics, which facilitates data-intensive, multidisciplinary team science to improve the health of patients and populations in Kentucky and beyond. Talbert will also serve as the division chief for biomedical informatics in the College of Medicine. A national leader in informatics research at the intersection of evidence-based policy and health care outcomes, Talbert has been at the forefront of building UK’s biomedical informatics (BMI) capacity over the last 25 years.

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Dr. Jin Chen receives funds to standardize and normalize CT images for lung cancer patients

Oct 11, 2019

Dr. Jin Chen receives two-year funds from NCI to standardize and normalize CT images for lung cancer patients. Computed tomography (CT) is one of the most popular diagnostic image modalities routinely used for assessing anatomical tissue characteristics for disease management. However, CT images are often acquired using scanners from different vendors with different imaging standards, posing a fundamental challenge to radiomic studies across sites. The goal of the Standardization and Normalization of CT images for lung cancer patients (STAN-CT) project is to develop a deep learning software package that can automatically standardize and normalize a large volume of chest CT images to facilitate cross-site large-scale image feature extraction for lung cancer characterization and stratification.

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New journal article describes identifying emerging phenomenon from experiments

Sep 06, 2019

A new journal article, Identifying emerging phenomenon in long temporal phenotyping experiments, from Dr. Chen's group describes an algorithm to identify emerging phenomemna, i.e., a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time period, from from large-scale temporal plant phenotyping experiments.  The paper was published in Bioinformatics, a leading journal in the field.

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Informatics framework for graphics libraries presented at ontology conference

Jul 31, 2019

Work by Melissa Clarkson and Steve Roggenkamp presented at the 10th International Conference on Biomedical Ontology.

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Upcoming events

Challenges and Opportunities in Plant Science

Seung Yon (Sue) Rhee PhD

Nov 12, 2020|12:00 PM – 1:00 PM

Plants make up the biggest biotic component of the biosphere and play essential roles in all ecosystems. Our survival and well-being depend on plants and this dependence will increase as the climate changes rapidly. To improve how we obtain food, energy, and materials from plants and steward the health of our environment for future generations, we need to understand how plants work at multiple scales from molecules to cells to ecosystems. A major challenge to achieving this goal is a limited understanding of functions of plant genes. The majority of genes in plant genomes are uncharacterized and many of them are found only in plant lineages. Traditional sequence-similarity based biochemical function inference cannot address this challenge. Another aspect of gene function that is critical but generally lacking is the spatial and temporal context under which gene products operate. These challenges have, in part, driven the spectacular advances and inventions in genomics, imaging, mass spectrometry and we are now capable of high-throughput, high-content, and high-resolution measurements of gene and protein function parameters. Along with these technologies and emerging datasets, we need advances in computational biology and bioinformatics tools, concepts, and methods. In this talk, I will describe these challenges and some of the efforts we are making in addressing them.

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Past events

Perspective on Deep Imaging

Ge Wang PhD

Mar 05, 2020|12:00 PM – 1:00 PM

130 University Health Services Building (Pizza & drinks served at 11:50) Please RSVP to BMI@uky.edu

Artificial intelligence is now recognized as an on-going paradigm shift, with an emphasis on machine learning especially deep learning. Computer vision and image analysis are two major applications of deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic imaging produces images of multi-dimensional structures from experimentally measured data (line integrals, harmonic components, and so on, of underlying images) which are tomographic features (features to images). Since 2016, deep learning is being actively developed worldwide for tomographic imaging, forming a new area of imaging research. In this presentation, we present a perspective on deep imaging involving data processing, image reconstruction, radiomics, and beyond. We show deeptomographic results and also explore network innovations.

Slides from presentations:

Perspective on Deep Imaging

Medical Imaging in the Deep Learning Framework

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Accelerating Bioinformatics Applications using GPUs

Fernanda Foertter PhD

Feb 20, 2020|12:00 PM – 1:00 PM

University Health Services 130 (pizza and drinks at 11:50 - RSVP to BMI@uky.edu)

 

The biological sciences are currently experiencing an increase in data size and complexity. Traditional methods are now reaching the point they can no longer keep up with the data output. Larger datasets, particularly when combined with other data sources, open opportunities to apply methods like deep learning on these complex data. NVIDIA is contributing to these needs by developing algorithms that can leverage graphics processing units (GPUs). These problems benefit from high memory bandwidth and very high parallelism inherent of the GPU architecture. This talk will explore some of the collaborations we have with industry and academia and share some of what our research team is currently working on.

GPUs for Genomics

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