NSF Project: IIS 0905117
III: Medium: Collaborative Research: Computational Methods to Advance Chemical Genetics by Bridging Chemical and Biological Spaces
Funded by: National Science Foundation (NSF)
Duration: Sept 1, 2009 - Aug 31, 2013 (Estimated)
NSF Award 0905117
Principal Investigator:
Graduate Students:
- Samuel Blasiak
- Anveshi Charuvaka
- Tanwishta Saha
- Sheng Li (Alumnus)
Undergraduate Students:
- Charles Sweet
Abstract: The recent development of various government and University funded screening centers has provided the academic research community with access to state-of-the-art high-throughput and high-content screening facilities. As a result, chemical genetics, which uses small organic molecules to alter the function of proteins, has emerged as an important experimental technique for studying and understanding complex biological systems. However, the methods used to develop small-molecule modulators (chemical probes) of specific protein functions and analyze the phenotypes induced by them have not kept pace with advances in the experimental screening technologies. Developing probes for novel protein targets remains a laborious process, whereas experimental approaches to identify the proteins that are responsible for the phenotypes induced by small molecules require a large amount of time and capital expenditure. There is a critical need to develop new methods for probe development and target identification and make them publicly available to the research community. Lack of such tools represents an important problem as it impedes the identification of chemical probes for various proteins and reduces our ability to effectively analyze the experimental results in order to elucidate the molecular mechanisms underlying biological processes.
Key Words: supervised learning; semi-supervised learning; cheminformatics; structural bioinformatics; data mining; graph algorithms
Publications:
Edited Book:
Introduction to Protein Structure Prediction: Methods and Algorithms
Huzefa Rangwala and George Karypis (eds.)
Wiley, 2010
Conference/Journal Publications:
- Huzefa Rangwala, Christopher Kauffman, George Karypis. svmPRAT: SVM-based Protein Residue Annotation Toolkit. BMC Bioinformatics, 10:439 (2009).
- Xia Ning, Huzefa Rangwala, George Karypis. Multi-Assay-Based Structure−Activity Relationship Models: Improving Structure−Activity Relationship Models by Incorporating Activity Information from Related Targets". Journal of Chemical Information and Modeling, 49(11); 2444–2456 (2009).
- Rezwan Ahmed, Huzefa Rangwala, and George Karypis. TOPTMH: Topology Predictor for Transmembrane Alpha Helices. Journal of Bioinformatics and Computational Biology, 8(1); 39-57 (2010).
- Huzefa Rangwala, Multiple Kernel Learning for Fold Recognition. Proceedings of 2nd ISCA Bioinformatics and Computational Biology Conference (BiCoB), Honolulu, Hawaii, USA (March 24-26 2010).
- Sheng Li, Huzefa Rangwala, An Information Theoretic Analysis of RNA and DNA Binding Sites. Proceedings of 3rd ISCA International Conference on Bioinformatics and Computational Biology (BICoB), New Orleans, LA (March 2011).
- Sam Blasiak, Huzefa Rangwala. A Hidden Markov Model variant for sequence classification. To Appear in the 22nd International Joint Conference in Artificial Intelligence (IJCAI), Barcelon, Spain (July 2011).
- Pu Wang, Carlotta Domeniconi, Huzefa Rangwala, Kathryn Laskey. Feature Enriched Nonparametric Bayesian Co-clustering. (Under Review).
- Emma Dixon, Cynthia Clubb, Sara Pittman, Larry Ammann, Zeehasham Rasheed, Nazia Kazmi, Ali Keshavarzian, Patrick Gillevet, Huzefa Rangwala, Robin D Couch. Solid-phase microextraction and the human fecal VOC metabolome, PLOS ONE (In Press).
- Ronak Shah, Huzefa Rangwala, Nadine Kabbani. A Bioinformatic Analysis of the D2 Dopamine Receptor Reveals Patterns in Domain Evolution, BIOLOGICAL BULLETIN (Under Review).
