Biomedical subjects

Suchendra M Bhandarkar

Publications and source records attributed to Suchendra M Bhandarkar.

3 recordsLinked to original sources

PubMed2003

A comparison of physical mapping algorithms based on the maximum likelihood model.

MOTIVATION: Physical mapping of chromosomes using the maximum likelihood (ML) model is a problem of high computational complexity entailing both discrete optimization to recover the optimal probe order as well as continuous optimization to recover the optimal inter-probe spacings. In this paper, two versions of the genetic algorithm (GA) are proposed, one with heuristic crossover and deterministic replacement and the other with heuristic crossover and stochastic replacement, for the physical mapping problem under the maximum likelihood model. The genetic algorithms are compared with two other discrete optimization approaches, namely simulated annealing (SA) and large-step Markov chains (LSMC), in terms of solution quality and runtime efficiency. RESULTS: The physical mapping algorithms based on the GA, SA and LSMC have been tested using synthetic datasets and real datasets derived from cosmid libraries of the fungus Neurospora crassa. The GA, especially the version with heuristic crossover and stochastic replacement, is shown to consistently outperform the SA-based and LSMC-based physical mapping algorithms in terms of runtime and final solution quality. Experimental results on real datasets and simulated datasets are presented. Further improvements to the GA in the context of physical mapping under the maximum likelihood model are proposed. AVAILABILITY: The software is available upon request from the first author.

Algorithms↗

PubMed2002

Parallel Monte Carlo methods for physical mapping of chromosomes.

Reconstructing a physical map of a chromosome from a genomic library presents a central computational problem in genetics. Physical map reconstruction in the presence of errors is a problem of high computational complexity. Parallel Monte Carlo methods for a maximum likelihood estimation-based approach to physical map reconstruction are presented. The estimation procedure entails gradient descent search for determining the optimal spacings between probes for a given probe ordering. The optimal probe ordering is determined using a simulated Monte Carlo algorithm. A two-tier parallelization strategy is proposed wherein the gradient descent search is parallelized at the lower level and the simulated Monte Carlo algorithm is simultaneously parallelized at the higher level. Implementation and experimental results on a network of shared-memory symmetric multiprocessors (SMPs) are presented.

Algorithms↗

PubMedDate not supplied

Human motion capture data compression by model-based indexing: a power aware approach.

Human Motion Capture (MoCap) data can be used for animation of virtual human-like characters in distributed virtual reality applications and networked games. MoCap data compressed using the standard MPEG-4 encoding pipeline comprising of predictive encoding (and/or DCT decorrelation), quantization, and arithmetic/Huffman encoding, entails significant power consumption for the purpose of decompression. In this paper, we propose a novel algorithm for compression of MoCap data, which is based on smart indexing of the MoCap data by exploiting structural information derived from the skeletal virtual human model. The indexing algorithm can be fine-controlled using three predefined quality control parameters (QCPs). We demonstrate how an efficient combination of the three QCPs results in a lower network bandwidth requirement and reduced power consumption for data decompression at the client end when compared to standard MPEG-4 compression. Since the proposed algorithm exploits structural information derived from the skeletal virtual human model, it is observed to result in virtual human animation of visually acceptable quality upon decompression.

Algorithms↗