Publications

L. Rojo-Arreola, T. Long, D. Asarnow, B.M. Suzuki, R. Singh and C.R. Caffrey, "Chemical and genetic validation of the statin drug target for the potential treatment of the helminth disease, schistosomiasis," PLoS ONE, 9(1): e87594, 2014. (Corresponding author C.R. Caffrey).
D. Asarnow and R. Singh, "Segmenting the Etiological Agent of Schistosomiasis for High-Content Screening," IEEE Transactions on Medical Imaging, vol. 32, no. 6, pp. 1007-10018, 2013. (Corresponding author R. Singh).
R. Singh, "Quantitative High-Content Screening-Based Drug Discovery against Helminthic Diseases", in Parasitic Helminths: Targets, Screens, Drugs, and Vaccines, Ed. C. Caffrey, Wiley-Blackwell, pp. 159-179 2012.
U. Saha and R. Singh, "Vision-Based Tracking of Complex Macroparasites for High-Content Phenotypic Drug Screening", International Symposium on Visual Computing (ISVC), Lecture Notes in Computer Science, Vol. 7432, pp. 104-114, Springer, 2012.
D. Asarnow and R. Singh, "Segmentation of Parasites for High-Content Screening using Phase Congruency and Grayscale Morphology", International Symposium on Visual Computing (ISVC), Lecture Notes in Computer Science, Vol. 7431, pp. 51-60, Springer, 2012.
H. Lee*, A. Moody-Davis, U. Saha, B. Suzuki, D. Asarnow, S. Chen, M. Arkin, C. Caffrey, and R. Singh*, "Quantification and Clustering of Phenotypic Screening Data Using Time Series Analysis for Chemotherapy of Schistosomiasis", BMC Genomics, 12 (Suppl 1):S4, 2012 (*H. Lee and R. Singh equal contributors. Corresponding author R. Singh).
A. Moody-Davis, L. Mennillo and R. Singh, "Region Based Segmentation of Parasites for High-Throughput Screening," G. Bebis et al. (Eds.): International Symposium on Visiual Computing, Part I, LNCS 6938, pp. 44-54, 2011.
R. Singh, V. Popescu, L. Mennillo, B. Suzuki, and C. Caffrey, "Association Rule Discovery in Time-Series Phenomic Data", Bioimage Informatics, Carnegie-Mellon University, 2010 (peer-reviewed poster).
R. Singh, M. Pittas, I. Heskia, F. Xu, J. H. McKerrow, and C. Caffrey, "Automated Image-Based Phenotypic Screening for High-Throughput Drug Discovery", IEEE Symposium on Computer-Based Medical Systems (CBMS), pp. 1-8, 2009.
R. Singh, M. Pittas, I. Heskia, F. Xu, J. H. McKerrow, and C. Caffrey, "Automated Image-Based Phenotypic Screening of Multi-Cellular Pathogens for High-Throughput Drug Discovery", Bioimage Informatics, Howard Hughes Medical Institute, Janelia Farm, 2009 (peer-reviewed poster).

Computational Drug Discovery against Schistosomiasis

Description

Schistosomiasis or bilharzia is a disease that afflicts more than 200 million people around the world with 20 million suffering severe effects and places over 600 million people at risk. Schistosomiasis ranks second only behind malaria, in terms of socio-economic and public health impact in developing countries. This disease is caused by parasitic worms of the genus Schistosoma which live in fresh water snails. The larval forms of the parasites have the ability to penetrate the skin and thus infect people who come into contact with contaminated water. The disease has been classified as one of the seventeen neglected tropical diseases by the Centers for Disease Control and Prevention and the World Health Organization. As such, it causes great misery both through physical suffering and by depriving people of their health and economic potential. The objective of our research is to use Computer Science in novel ways to develop a quantitative and mechanistic understanding of schistosomiasis and accelerate the development of therapeutics against this disease.

Our research foci include: (1) image and video-based phenotype analysis and identification, (2) development of computational phenomics-based high-throughput drug screening methods (3) designing algorithms for structure-activity modeling, given the phenomic responses of the parasite to putative drugs, (4) designing novel algorithms and user-data interaction paradigms for mining phenotype-phenotype and phenotype-structure relationships.