Name
Abstract | ||
|---|---|---|
We describe a new statistically based algorithm that aligns sequences by means of predictive inference. Using residue frequencies, this Gibbs sampling algorithm iteratively selects alignments in accordance with their conditional probabilities. The newly formed alignments in tum update an evolving residue frequency model. When equilibrium is reached the most probable alignment can be identified. If a detectable pattem is present, generally convergence is rapid. Effectively, the algorithm finds optimal local multiple alignments in linear time (seconds on current workstations). Its use is illustrated on test sets of lipocalins and prenyltranferases. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1109/HICSS.1994.323572 | HICSS |
Keywords | Field | DocType |
image recognition,inference mechanisms,medical image processing,probability,proteins,Gibbs sampler,conditional probabilities,detectable pattern,evolving residue frequency model,lipocalins,multiple sequences,optimal local multiple alignments,predictive inference,prenyltranferases,protein sequences,residue frequencies,statistically based algorithm,subtle motifs | Convergence (routing),Conditional probability,Pattern recognition,Computer science,Predictive inference,Artificial intelligence,Time complexity,Gibbs sampling | Conference |
Volume | Citations | PageRank |
5 | 2 | 1.15 |
References | Authors | |
4 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Charles E. Lawrence | 1 | 78 | 21.32 |
| Stephen F Altschul | 2 | 180 | 26.55 |
| John C. Wootton | 3 | 233 | 42.66 |
| Mark S. Boguski | 4 | 93 | 70.98 |
| Andrew F. Neuwald | 5 | 35 | 4.83 |
| Jun S. Liu | 6 | 998 | 162.67 |