Name
Abstract | ||
|---|---|---|
Flow cytometry is a common technique for quantitatively measuring the expression of individual molecules on cells. The molecular expression is represented by a frequency histogram of fluorescence intensity. For flow cytometry to be used as a knowledge discovery tool to identify unknown molecules, histogram comparison is a major limitation. Many traditional comparison methods do not provide adequate assessment of histogram similarity and molecular relatedness. We have explored a new, approach-applying information theory to histogram comparison, and tested it with histograms from 14 antibodies over 3 cell types. The information theory approach was able to improve over traditional methods by recognizing various non-random correlations between histograms in addition to similarity and providing a quantitative assessment of similarity beyond hypothesis testing of identity. |
Year | Venue | Keywords |
|---|---|---|
2001 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | information theory,normal distribution,statistical distributions,flow cytometry,molecular biology |
Field | DocType | Issue |
Information theory,Histogram,Normal distribution,Pattern recognition,Flow cytometry,Computer science,Algorithm,Probability distribution,Artificial intelligence,Knowledge extraction,Quantitative assessment,Statistical hypothesis testing | Conference | SUPnan |
ISSN | Citations | PageRank |
1067-5027 | 1 | 0.52 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Zeng | 1 | 547 | 67.98 |
| A J Young | 2 | 1 | 0.52 |
| Aziz A. Boxwala | 3 | 585 | 72.72 |
| James Rawn | 4 | 7 | 2.10 |
| W Long | 5 | 1 | 0.52 |
| M. P. Wand | 6 | 51 | 10.35 |
| M Salganik | 7 | 1 | 0.52 |
| Edgar Milford | 8 | 6 | 1.73 |
| Steven J Mentzer | 9 | 21 | 3.93 |
| Robert Greenes | 10 | 644 | 106.18 |