Abstract

Drs. Kutlar and Woods have compiled a set of data from sickle cell anemia patients. We have used artificial neural network technology to determine, in principle, whether it can be decided from the initial condition of a patient if he/she will respond to hydroxyurea treatment, and if so, at what dosage of the drug. Another goal of the project is to predict the course of development of the disease by monitoring patient data with a neural network predictor. During this past year, we have been able to train an artificial neural network that can predict with 85% accuracy whether or not a patient is going to respond positively to the treatment. We feel that this number can be improved by further investigation. A paper is in preparation for submission to the Journal of the American Medical Association.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR005351-13
Application #
6493648
Study Section
Project Start
2001-08-01
Project End
2002-07-31
Budget Start
Budget End
Support Year
13
Fiscal Year
2001
Total Cost
$288,015
Indirect Cost

  Institution

Name
University of Georgia
Department
Type
DUNS #
City
Athens
State
GA
Country
United States
Zip Code
30602

  Publications

Hannides, Angelos K; Aller, Robert C (2016) Priming effect of benthic gastropod mucus on sedimentary organic matter remineralization. Limnol Oceanogr 61:1640-1650
Revoredo, Leslie; Wang, Shengjun; Bennett, Eric Paul et al. (2016) Mucin-type O-glycosylation is controlled by short- and long-range glycopeptide substrate recognition that varies among members of the polypeptide GalNAc transferase family. Glycobiology 26:360-76
Zhao, Wujun; Zhu, Taotao; Cheng, Rui et al. (2016) Label-Free and Continuous-Flow Ferrohydrodynamic Separation of HeLa Cells and Blood Cells in Biocompatible Ferrofluids. Adv Funct Mater 26:3990-3998
Wu, Liang; Viola, Cristina M; Brzozowski, Andrzej M et al. (2015) Structural characterization of human heparanase reveals insights into substrate recognition. Nat Struct Mol Biol 22:1016-22
Qiu, Hong; Xiao, Wenyuan; Yue, Jingwen et al. (2015) Heparan sulfate modulates Slit3-induced endothelial cell migration. Methods Mol Biol 1229:549-55
Li, Zixuan; Moniz, Heather; Wang, Shuo et al. (2015) High structural resolution hydroxyl radical protein footprinting reveals an extended Robo1-heparin binding interface. J Biol Chem 290:10729-40
Czuchry, Diana; Desormeaux, Paul; Stuart, Melissa et al. (2015) Identification and Biochemical Characterization of the Novel ?2,3-Sialyltransferase WbwA from Pathogenic Escherichia coli Serotype O104. J Bacteriol 197:3760-8
Liu, Lin; Zha, Jingying; DiGiandomenico, Antonio et al. (2015) Synthetic Enterobacterial Common Antigen (ECA) for the Development of a Universal Immunotherapy for Drug-Resistant Enterobacteriaceae. Angew Chem Int Ed Engl 54:10953-7
Zhang, Fuming; Moniz, Heather A; Walcott, Benjamin et al. (2014) Probing the impact of GFP tagging on Robo1-heparin interaction. Glycoconj J 31:299-307
Zarnowski, Robert; Westler, William M; Lacmbouh, Ghislain Ade et al. (2014) Novel entries in a fungal biofilm matrix encyclopedia. MBio 5:e01333-14

Showing the most recent 10 out of 245 publications