Name
Abstract | ||
|---|---|---|
Polony technology is a low-cost, high-throughput platform employed in several applications such as DNA sequencing, haplotyping and alternative pre-mRNA splicing analysis. Owing to their random placement, however, overlapping polonies occur often and may result in inaccurate or unusable data. Accurately identifying polony positions and sizes is essential for maximizing the quantity and quality of data aquired in an image, however, most existing identification algorithms do not handle overlapping polonies well. In this paper, we present a novel polony identification approach combining both a Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. Experiments on simulated and real images of highly overlapping polonies show that our algorithm has a 10% to 20% increase in recall compared with the existing algorithms, while keeping precision at the same level. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BIBE.2010.43 | BIBE |
Keywords | Field | DocType |
gaussian mixture model,polony identification,expectation-maximisation algorithm,high-throughput platform,em,haplotyping,existing algorithm,expectation-maximization algorithm,existing identification algorithm,dna sequencing,polony technology,novel polony identification approach,overlapping polony,biological techniques,biology computing,molecular biophysics,random placement,alternative pre-mrna splicing analysis,gaussian processes,polony position,alternative pre-mrna,dna,unusable data,em algorithm,high temperature superconductors,polymers,high throughput,pixel,expectation maximization,expectation maximization algorithm,gaussian distribution,dna sequence | Expectation–maximization algorithm,Computer science,Gaussian process,Artificial intelligence,Pixel,Real image,Bioinformatics,Mixture model,Machine learning,Polony | Conference |
ISBN | Citations | PageRank |
978-1-4244-7494-3 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Li | 1 | 883 | 93.88 |
| Paul M. Ruegger | 2 | 8 | 0.68 |
| James Borneman | 3 | 67 | 8.33 |
| Tao Jiang | 4 | 1809 | 155.32 |