Kevin Horan

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BACKGROUND Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins. (More)
MOTIVATION The ability to accurately measure structural similarities among small molecules is important for many analysis routines in drug discovery and chemical genomics. Algorithms used for this purpose include fragment-based fingerprint and graph-based maximum common substructure (MCS) methods. MCS approaches provide one of the most accurate similarity(More)
Acknowledgments I am deeply grateful to my adviser, Dr. Christian R. Shelton, for his invaluable mentoring. Without his help I would not have been able to complete this dissertation. I thank my committee members: Dr. Lonardi for his technical advise, Dr. Hanne-man for his time and advice with social network analysis, and Dr. Keogh for his generous support(More)
  • Antony M Lam, Christian R Shelton, K Amit, Eamonn Roy-Chowdhury, Keogh, Committee Chairperson +12 others
  • 2010
Acknowledgements Doing a PhD has been a challenging but very rewarding experience. In my years as a student, I have had the good fortune of having many kind and supportive people in my life. Roy-Chowdhury for without their guidance and patience, I would not be where I am today. The list of things they both have done for me would be too long to show here but(More)
  • Yu Fan, Christian R Shelton, Gianfranco Ciardo, Neal E Young, Committee Chairperson, Kin Fai Kan +9 others
  • 2009
Acknowledgments There are many people to whom I owe many thanks for helping me going through this long process of completing a Ph.D. First and foremost, I would like to express my gratitude to my advisor, Dr. Christian R. Shelton, for his unending support, extremely constructive feedback, excellent supervision, and all the encouragement over the last five(More)
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