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Displaying 1-16 out of 16 total
Visualization Challenges for a New Cyberpharmaceutical Computing Paradigm
Found in: Parallel and Large-Data Visualization and Graphics, IEEE Symposium on
By Russell J. Turner, Kabir Chaturvedi, Nathan J. Edwards, Daniel Fasulo, Aaron L. Halpern, Daniel H. Huson, Oliver Kohlbacher, Jason R. Miller, Knut Reinert, Karin A. Remington, Russell Schwartz, Brian Walenz, Shibu Yooseph, Sorin Istrail
Issue Date:October 2001
pp. 7-18
In recent years, an explosion in data has been profoundly changing the field of biology and creating the need for new areas of expertise, particularly in the handling of data. One vital area that has so far received insufficient attention is how to communi...
 
Algorithms for Association Study Design Using a Generalized Model of Haplotype Conservation
Found in: Computational Systems Bioinformatics Conference, International IEEE Computer Society
By Russell Schwartz
Issue Date:August 2004
pp. 90-97
There is considerable interest in computational methods to assist in the use of genetic polymorphism data for locating disease-related genes. Haplotypes, contiguous sets of correlated variants, may provide a means of reducing the difficulty of the data ana...
 
Haplotype Motifs: An Algorithmic Approach to Locating Evolutionarily Conserved Patterns in Haploid Sequences
Found in: Computational Systems Bioinformatics Conference, International IEEE Computer Society
By Russell Schwartz
Issue Date:August 2003
pp. 306
The promise of plentiful data on common human genetic variations has given hope that we will be able to uncover genetic factors behind common diseases that have proven difficult to locate by prior methods. Much recent interest in this problem has focused o...
 
Novel Multisample Scheme for Inferring Phylogenetic Markers from Whole Genome Tumor Profiles
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Ayshwarya Subramanian,Stanley Shackney,Russell Schwartz
Issue Date:November 2013
pp. 1422-1431
Computational cancer phylogenetics seeks to enumerate the temporal sequences of aberrations in tumor evolution, thereby delineating the evolution of possible tumor progression pathways, molecular subtypes, and mechanisms of action. We previously developed ...
 
Coalescent-Based Method for Learning Parameters of Admixture Events from Large-Scale Genetic Variation Data
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Ming-Chi Tsai,Guy Blelloch,R. Ravi,Russell Schwartz
Issue Date:September 2013
pp. 1137-1149
Detecting and quantifying the timing and the genetic contributions of parental populations to a hybrid population is an important but challenging problem in reconstructing evolutionary histories from genetic variation data. With the advent of high throughp...
 
A Consensus Tree Approach for Reconstructing Human Evolutionary History and Detecting Population Substructure
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Ming-Chi Tsai, Guy Blelloch, R. Ravi, Russell Schwartz
Issue Date:July 2011
pp. 918-928
The random accumulation of variations in the human genome over time implicitly encodes a history of how human populations have arisen, dispersed, and intermixed since we emerged as a species. Reconstructing that history is a challenging computational and s...
 
Network-Based Inference of Cancer Progression from Microarray Data
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Yongjin Park, Stanley Shackney, Russell Schwartz
Issue Date:April 2009
pp. 200-212
Cancer cells exhibit a common phenotype of uncontrolled cell growth, but this phenotype may arise from many different combinations of mutations. By inferring how cells evolve in individual tumors, a process called cancer progression, we may be able to iden...
 
Mixed Integer Linear Programming for Maximum-Parsimony Phylogeny Inference
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Srinath Sridhar, Fumei Lam, Guy E. Blelloch, R. Ravi, Russell Schwartz
Issue Date:July 2008
pp. 323-331
Reconstruction of phylogenetic trees is a fundamental problem in computational biology. While excellent heuristic methods are available for many variants of this problem, new advances in phylogeny inference will be required if we are to be able to continue...
 
Algorithms for Efficient Near-Perfect Phylogenetic Tree Reconstruction in Theory and Practice
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Srinath Sridhar, Kedar Dhamdhere, Guy Blelloch, Eran Halperin, R. Ravi, Russell Schwartz
Issue Date:October 2007
pp. 561-571
We consider the problem of reconstructing near-perfect phylogenetic trees using binary character states (referred to as BNPP). A perfect phylogeny assumes that every character mutates at most once in the evolutionary tree, yielding an algorithm for binary ...
 
Epitope Prediction Algorithms for Peptide based Vaccine Design
Found in: Computational Systems Bioinformatics Conference, International IEEE Computer Society
By Liliana Florea, Bjarni Halldórsson, Oliver Kohlbacher, Russell Schwartz, Stephen Hoffman, Sorin Istrail
Issue Date:August 2003
pp. 17
Peptide-based vaccines, in which small peptides derived from target proteins (epitopes) are used to provoke an immune reaction, have attracted considerable attention recently as a potential means both of treating infectious diseases and promoting the destr...
 
Novel Multisample Scheme for Inferring Phylogenetic Markers from Whole Genome Tumor Profiles
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Ayshwarya Subramanian, Russell Schwartz, Stanley Shackney
Issue Date:November 2013
pp. 1422-1431
Computational cancer phylogenetics seeks to enumerate the temporal sequences of aberrations in tumor evolution, thereby delineating the evolution of possible tumor progression pathways, molecular subtypes, and mechanisms of action. We previously developed ...
     
Coalescent-Based Method for Learning Parameters of Admixture Events from Large-Scale Genetic Variation Data
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Guy E. Blelloch, Ming-Chi Tsai, R. Ravi, Russell Schwartz
Issue Date:September 2013
pp. 1137-1149
Detecting and quantifying the timing and the genetic contributions of parental populations to a hybrid population is an important but challenging problem in reconstructing evolutionary histories from genetic variation data. With the advent of high throughp...
     
A Consensus Tree Approach for Reconstructing Human Evolutionary History and Detecting Population Substructure
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Guy E. Blelloch, Ming-Chi Tsai, R. Ravi, Russell Schwartz
Issue Date:July 2011
pp. 918-928
The random accumulation of variations in the human genome over time implicitly encodes a history of how human populations have arisen, dispersed, and intermixed since we emerged as a species. Reconstructing that history is a challenging computational and s...
     
Approximation algorithms for speeding up dynamic programming and denoising aCGH data
Found in: Journal of Experimental Algorithmics (JEA)
By Gary L. Miller, Maria A. Tsiarli, Russell Schwartz, Charalampos E. Tsourakakis, Richard Peng
Issue Date:May 2011
pp. 1.1-1.27
The development of cancer is largely driven by the gain or loss of subsets of the genome, promoting uncontrolled growth or disabling defenses against it. Denoising array-based Comparative Genome Hybridization (aCGH) data is an important computational probl...
     
Network-Based Inference of Cancer Progression from Microarray Data
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Russell Schwartz, Stanley Shackney, Yongjin Park
Issue Date:April 2009
pp. 200-212
Cancer cells exhibit a common phenotype of uncontrolled cell growth, but this phenotype may arise from many different combinations of mutations. By inferring how cells evolve in individual tumors, a process called cancer progression, we may be able to iden...
     
Haplotypes and informative SNP selection algorithms: don't block out information
Found in: Proceedings of the seventh annual international conference on Computational molecular biology (RECOMB '03)
By Andrew G. Clark, Bjarni V. Halldorsson, Russell Schwartz, Sorin Istrail, Vineet Bafna
Issue Date:April 2003
pp. 19-27
It is widely hoped that variation in the human genome will provide a means of predicting risk of a variety of complex, chronic diseases. A major stumbling block to the successful identification of association between human DNA polymorphisms (SNPs) and vari...
     
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