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.


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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Two papers accepted by computer vision conference

Apr 08, 2019

Two papers from the BMI Associate Professor Qiang Cheng's group have been accepted by IEEE CVPR2019, June 16th - 20th 2019, the premier annual computer vision conference.

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Kavuluru named to editorial board of Journal of Biomedical Informatics

Feb 28, 2019

Rama Kavuluru, PhD, has joined the editorial board of the Journal of Biomedical Informatics, a journal devoted to publishing new biomedical informatics methodologies.

IBI members to present at AMIA Annual Symposium

Sep 02, 2018

The AMIA 2018 Annual Symposium will be held in San Francisco, November 3–7. Accepted work from IBI members will be presented as oral presentations, system demonstrations, posters, and in pre-symposium working groups.

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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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Promoting Transparency in Biomedical Publications Using Natural Language Processing

Halil Kilicoglu PhD

Feb 06, 2020|12:00 PM – 1:00 PM

130 University Health Services Building, Please RSVP to BMI@uky.edu


Rigor, reproducibility, and transparency of biomedical research has been the topic of much debate in recent years. Some ongoing efforts aim to address the issues in research conduct and dissemination by focusing on standardization and guideline development. As biomedical research output increases exponentially, automated tools are needed to complement such efforts and assist the stakeholders (e.g., scientists, journals, funding organizations) in assessing and improving research output.

Textual artifacts (e.g., grant applications, study protocols, manuscripts, and publications) are central to biomedical communication. With the progress made in in biomedical language processing and text mining (bioNLP) in recent years, it is timely to ask if and how bioNLP techniques can be used to help address some of the rigor and integrity problems manifested in these artifacts. In this talk, I will motivate the use of bioNLP methods toward these goals by providing some use cases, and discuss a couple of tasks that I have recently pursued in this area: a) assessing clinical trial publications for reporting guideline adherence, and b) elucidating contradictory claims in biomedical publications. I will also highlight some of the challenges facing bioNLP research focusing on this area.

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