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Efficient Algorithms for Radiation Therapy
Dynamic multileaf collimator intensity modulated radiation therapy is used to deliver intensity modulated beams on a collimator with the multiple leaves in motion. In joint work with Sahni, with support from a NIH grant, we have shown that optimal leaf sequencing based on unidirectional movement of the collimator leaves is as monitor unit (MU) efficient as bi-directional movement of these leaves. We have also developed efficient algorithms for segmental collimator beam delivery that completely eliminates areas of under dosages due to practical tongue-and-groove effect between adjacent leafs. Our methods results in 10% to 20% decrease in total MU as compared to field splitting techniques used in commercial planning systems. Data Mining Algorithms for CGH Data
Numerical and structural chromosomal imbalances are one of the most prominent and pathogenetically relevant features of neoplastic cells. One method for measuring genomic aberrations is Comparative Genomic Hybridization (CGH). CGH is a analysis method for detecting regions with genomic imbalances (gains or losses of DNA segments).I am developing novel data mining based algorithms for aiding cancer detection and classification sing Comparative Genomic Hybridization (CGH). The goal is develop techniques to find the key genetic intervals that can predict the type of cancer. Our preliminary results show that we can significantly improve on existing techniques. Novel Algorithms for Drug Design Traditional pharmacological drug discovery approaches focused more on the therapeutic effects of drugs than their side-effects. Recent advances in bioinformatics have fostered rational drug development methods that aim to reduce serious side-effects. The first step in this approach is the identification of specific biological drug targets (enzymes or proteins), which can be manipulated to produce the desired effect (of curing a disease) with minimum disruptive side-effects. We are developing algorithms for the pharmacological problem of identifying the optimal enzyme-combination (i.e., drug targets) whose inhibition will achieve the required effect of eliminating a given target set of compounds, while incurring minimal side-effects. We propose two approaches to solve the problem. Data Mining for Microbial Resistance
The current arsenal of antimicrobial or antibiotic drugs for treating bacterial infection is one of the most important public health tools available, but it is not an inexhaustible resource. The more haphazardly antimicrobial drugs are used, the more the targeted pathogens develop resistance. Once a pathogen develops resistance to all of the available drugs, treating an infected patient may become difficult or impossible. This project is a collaboration between computer scientists and health scientists aimed at developing data mining tools for discovering when and why antimicrobial resistance appears in nosocomial (hospital acquired) infections.
Publications Srijit Kamath, Sartaj Sahni, Sanjay Ranka, Jonathan Li, and Jatinder Palta, Generalized field-splitting algorithms for optimal IMRT delivery efficiency, Physics in Medicine and Biology, 52, 2007, 5483-5496 (nominated for the 2008 Robbins Prize, given to the best paper in the journal of Physics in Medicine and Biology).
Srijit Kamath, Sartaj Sahni, Jatinder Palta, and Sanjay Ranka, Algorithms for optimal sequencing of dynamic multileaf collimators, Physics in Medicine and Biology, 49, 1, 2004, 33-54.
Srijit Kamath, Sartaj Sahni, Jatinder Palta, Sanjay Ranka, and Jonathan Li, Optimal leaf sequencing with elimination of tongue-and-groove underdosage, Physics in Medicine and Biology, 49, 3, 2004, N7-N19.
Srijit Kamath, Sartaj Sahni, Sanjay Ranka, Jonathan Li, and Jatinder Palta, A comparison of step-and-shoot leaf sequencing algorithms that eliminate tongue-and-groove effect, Physics in Medicine and Biology, 49, 2004, 3137-3143.
Srijit Kamath, Sartaj Sahni, Sanjay Ranka, Jonathan Li, and Jatinder Palta, Optimal field splitting for large intensity-modulated fields, Medical Physics, 31, 12, 2004, 3314-3323.
Srijit Kamath, Sartaj Sahni, Jonathan Li, Jatinder Palta, and Sanjay Ranka, Leaf sequencing algorithms for segmented multileaf collimation, Physics in Medicine and Biology, 48, 3, 2003, 307-324.
Jun Liu, Nirmalya Bandyopadhyay, Sanjay Ranka, Michael Baudis, Tamer Kahveci. Inferring Progression Models for CGH data, Journal of Bioinformatics, to appear
Jun Liu, Jaaved Mohammed, James Carter, Sanjay Ranka, Tamer Kahveci, and Michael Baudis, Distance-based clustering of CGH data. Bioinformatics, 22(16):1971–1978, 2006.
Jun Liu, Sanjay Ranka, and Tamer Kahveci,. Markers improve clustering of CGH data, Bioinformatics. 2007 Feb 15;23(4):450-7.
Padmavati Sridhar, Bin Song, Tamer Kahveci and Sanjay Ranka, Double Iterative Optimization for Metabolic Network-Based Drug Target Identification, International Journal of Data Mining and Bioinformatics, to appear.
Christopher M. Jermaine, Subramanian Arumugam, Abhijit Pol, Alin Dobra. "Scalable approximate query processing with the DBO engine," Proceedings of the ACM SIGMOD International Conference on Management of Data, 2007, p. 725. John Gums, Sanjay Ranka, Chris Jermaine. "Heterogenetiy in Resistance Trends Greatest in Large Hospitals: Results of the Antimicrobial Resistance Management Program," 47th Annual Interscience Conference on Antimicrobial Agents and Chemotherapy (ICAAC), v.47, 2007. John Gums, Sanjay Ranka, Christopher Jermaine. "Significant Heterogeneity Found in Resistance Trends Between Hospitals: Results of the Antimicrobial Resistance Management Program," 47th Annual Meeting of the Infectious Diseases Society of America (IDSA 2007), v.47, 2007. Manas Somaiya, Christopher M. Jermaine, Sanjay Ranka. "Learning correlations using the mixture-of-subsets model," ACM Transaction on Knowledge Discovery in Data, v.1, 2008. Mingxi Wu, Christopher M. Jermaine. "A Bayesian Method for Guessing the Extreme Values in a Data Set," Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB 2008), v.33, 2008, p. 471. S.M. Smith, J.G. Gums, C. Jermaine, S. Ranka. "The Implications of Phenotypic Clustering of Antimicrobial Resistance Patterns on Predicting Future Trends," 48th Annual ICAAC/46th Annual IDSA Meeting, Washington DC, 2008.
X. Song, Chris Jermaine, Sanjay Ranka, John Gums. "A Beyesian Mixture Model with Linear Regression Mixing Proportions," Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, v.14, 2008.
Xiuyao Song, Mingxi Wu, Christopher M. Jermaine, Sanjay Ranka. "Conditional Anomaly Detection," IEEE Trans. Knowl. Data Eng, v.19 (5), 2007, p. 631.
Xiuyao Song, Mingxi Wu, Christopher M. Jermaine, Sanjay Ranka. "Statistical Change Detection for Multi-Dimensional Data," Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), v.13, 2007, p. 667.
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