Year in Program: 4th

Hometown: Abidjan, Ivory Coast

Mentor: Dr. Naren

Email: amoakoja@mail.uc.edu

     Year in Program: 2nd

     Hometown:

     Mentors: Dr. Kalfa

     Email: azucencr@mail.uc.edu

Year in Program: 3rd

Hometown: 

Mentors: Dr. Reyes

Email: bohannab@mail.uc.edu

Year in Program: 3rd

Hometown: 

Mentors: Dr. Andorf

Email: eswarss@mail.uc.edu

Year in Program: 4th

Hometown: Cincinnati, Ohio

Mentor: Dr. Herman

Email: gloriuac@mail.uc.edu

Year in Program: 5th

Hometown: Istanbul, Turkey

Mentor: Dr. Hong

Email: gokerse@mail.uc.edu

Publications, Complete List at PubMed

Year in Program: 2nd

Hometown: 

Mentors: Dr. Ozbudak

Email: mcdanic6@mail.uc.edu

Year in Program: 5th

Hometown: Cincinnati, Ohio

Mentors: Drs. Heiny and Landero

Email: normanne@mail.uc.edu

I am interested in the secondary transport of ions using the Na+,K+-ATPase-established gradient in skeletal muscle tissue.

Publications, Complete List at PubMed

Year in Program: 3rd

Hometown: 

Mentors: Dr. Yu

Email: olatoktf@mail.uc.edu

Year in Program: 5th

Hometown: Wooster, Ohio

Mentor: Dr. Zhang

Email: parkecp@mail.uc.edu

I am primarily interested in Quantitative Systems Pharmacology (QSP) modeling and am currently looking into blood coagulation, hemophilia, and treatments. This modeling is primarily done with systems of differential equations, and I am most proficient at doing it in Python, though MATLAB or other languages can be used. Currently I am rotating in biomedical informatics, hopefully gaining more experience with deep learning or statistical programming techniques.

Year in Program: 5th

Hometown: Lima, Ohio

Mentor: Dr. Miraldi

Email: rizvifw@mail.uc.edu

While completing my Masters degree in Biomathematics at Ohio State University, I had the opportunity to take many interesting classes. One of these was Biomedical Informatics, taught by Dr. Kevin Coombes. In this course, I was really fascinated with the use of various data analysis techniques on biological data, such as Hierarchical Agglomerative Clustering and K-Means Clustering. In class, we used Hierarchical Clustering and Principal Component Analysis on RNA-Seq data of patients with prostate cancer. The data analysis showed that the data set was skewed and confounded by the chip type used. This result was not evident in the initial analysis of the data. Such analysis techniques have been essential in my Masters thesis project on sleep inertia in children. A research paper by Van Dongen et al. [1] finds that sleep-deprived patients can be divided into groups. These groups consist of patients who are found to be resilient, vulnerable, or somewhere in between to sleep deprivation. My Masters thesis advisor, Dr. Best, and I used these machine learning algorithms to evaluate data from a 10-minute visual psychomotor vigilance task test on children aged 5 to 12 to find an example of this characteristic. Further, we would like to generate a model which will be able to use baseline reaction times to predict reaction times of patients awakened from deep sleep (as could happen in the case of a fire alarm). As I graduated and moved on from Ohio State, my interest in machine learning and data analysis led me to the join the Miraldi Lab at Cincinnati Children's Hospital as a first-year student in the Systems Biology and Physiology PhD program at UC. In Dr. Miraldi's lab we use bioinformatics tools to help devise mathematical models to predict transcription factor activity, among other things. Dr. Miraldi and her colleagues were able to create the Inferelator in Th17 cells and predict transcription factor activity using sc-RNA-Seq data. A key input to the Inferelator is the prior matrix. This consists of known transcription factor and gene interactions which the program uses to help prune and predict new edges in the network. In my first semester in Dr. Miraldi's lab I helped generate a prior based on ChIP-seq experimental data available from Dr. Matt Weirauch's lab. With these, nearly 24,000 ChIP-seq experiments, we hope that the Inferelator will be able to perform at a much higher level (in regard to precision vs. recall). Even with much smaller data sets, the Inferelator is able to outperform current state-of-the-art techniques, so a larger data set should provide better precsion vs. recall curves. Moving forward, we hope to create a Convolutional Neural Network (CNN) that uses (sc)ATAC-seq data to predict ChIP-seq profiles. We are interested in using (sc)ATAC-seq as it requires significantly fewer cells than ChIP-seq.

[1] Van Dongen, H.P.A., Baynard, M.D., Maislin, G., and Dinges, D.F., Systematic Interindividual Differences in Neurobehavioral Impairment from Sleep Loss: Evidence of Trait-Like Differential Vulnerability, SLEEP 27.3 (2004), 423-433.

     Year in Program: 2nd

     Hometown:

     Mentor: Dr. Ozbudak

     Email: singhcl@mail.uc.edu

Year in Program: 4th

Hometown: Rajkot, Gujarat, India

Mentor: Dr. Salomonis

Email: thakkakk@mail.uc.edu

Year in Program: 5th

Hometown: Kathmandu, Nepal

Mentor: Dr. Crone

Email: thapaca@mail.uc.edu

Damage to the spinal cord can be caused in many ways ranging from motor vehicle accidents to recreational activities. Statistics provided by the National Spinal Cord Injury Statistical Center show that, as of 2015, about 12,500 new spinal cord injuries occur each year. According to the National Spinal Cord Injury Association, 8 of every 95 patients with complete spinal cord injuries above C3 die before receiving any medical treatment. The patients who survive are forever dependent on mechanical respirators to breathe. These statistics underscore the importance of research in spinal cord injury. Currently my research focus is in V2a interneurons. V2a are glutamatergic interneurons that are located within the ventral horn of the spinal cord and are found at all spinal levels. These interneurons are shown to be important for locomotion and breathing. A previous study in our lab showed that after C2 hemi-section, increasing the excitability of V2a neurons restored the activity of previously paralyzed diaphragm. This result substantiates the hypothesis that increasing the activity of V2a neurons may help in restoration of function after spinal cord injury. Similarly, another work in our lab revealed that increasing the activity of V2a neurons results in an increase in activity of auxiliary respiratory muscles (ARMs). However, silencing the activity of V2a neurons also leads to an increase in the activity of ARMs. These experiments showed that there are at least two types of V2a neurons: excitatory V2a neurons and inhibitory V2a neurons. To corroborate this finding, a recently published paper by Hayashi et.al. revealed 11 molecular distinct sub-groups of V2a neurons. Keeping these studies in mind, we are working on identifying expression of different molecules of V2a neurons that may help in identifying different types of V2a neurons.

    Year in Program: 1st

     Hometown:

     Mentors: Integrated-SBP

     Email: akinbooo@mail.uc.edu

    Year in Program: 1st

     Hometown:

     Mentors: Integrated-SBP

     Email: haygoodk@mail.uc.edu

     Year in Program: 1st

     Hometown:

     Mentors: Integrated-SBP

     Email: kuhnad@mail.uc.edu

Year in Program: 1st
Hometown:

Mentors: Integrated-SBP

Email: tronesle@mail.uc.edu