News

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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Events

Epigenomic insights of environmental disease

Zhibin Wang PhD

Dec 05, 2019|12:30 PM – 1:30 PM

130 University Health Services Building (Pizza & drinks served at 12:20. Please RSVP to BMI@uky.edu)

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Clinical Event Mining and Visualization for Chronic Rheumatic Diseases

Sujin Kim PhD

Nov 13, 2019|12:00 PM – 1:00 PM

UHS 130

Understanding the use of pattern mining algorithms and the development of a prototype system to visualize the relationships of frequent clinical events based on mined clinical sequences and associations.

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CANCELLED - Deciphering the regulatory code of metabolism in Arabidopsis - CANCELLED

Seung Yon (Sue) Rhee PhD

Nov 07, 2019|12:30 PM – 1:30 PM

Coordinated control of metabolic genes is crucial for plant development and adaptation to various environments. To discover general rules of metabolic regulation, we used omics data, machine learning, and experimental validation. To understand how metabolic genes are controlled by epigenetic marks, we mapped regulatory patterns of 16 epigenetic marks across metabolism in Arabidopsis and found a predominant regulatory pattern for specialized metabolic genes involved in controlling the precise timing of gene expression upon stimuli. To understand how transcription factors regulate metabolism, we constructed condition- and tissue-specific regulatory networks in Arabidopsis and identified patterns of transcriptional regulation at various levels. These studies elucidate metabolism coordination and contribute to developing toolkits for pathway engineering in plants.

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Reinforcement Learning for Brain Machine Interfaces

Jihye Bae PhD

Oct 17, 2019|12:30 PM – 1:30 PM

130 University Health Services Building

There have been promising advances in brain machine interfaces. However, many challenges still remain before this technology can become practical. In this talk, we will discuss some of the main issues along with possible approaches to overcome them. In particular, methods to translate neural signals to control external devices using reinforcement learning will be introduced.

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