Name
Abstract | ||
|---|---|---|
Beginning with the concept of near-optimal sequence alignments, we can assign a probability that each element in one sequence is paired in an alignment with each element in another sequence. This involves a sum over the set of all possible pairwise alignments, The method employs a designed hidden Markov model (HMM) and the rigorous forward and forward-backward algorithms of Rabiner. The approach can use any standard sequence-element-to-element probabilistic similarity measures and affine gap penalty functions. This allows the positional alignment statistical significance to be obtained as a function of such variables. A measure of the probabilistic relationship between any single sequence and a set of sequences can be directly obtained. In addition, the employed HMM with the Viterbi algorithm provides a simple link to the standard dynamic programming optimal alignment algorithms. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1089/cmb.1999.6.253 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
alignment statistics,hidden Markov model,HMM | Affine transformation,Sequence alignment,Dynamic programming,Pattern recognition,Gap penalty,Artificial intelligence,Bioinformatics,Probabilistic logic,Multiple sequence alignment,Hidden Markov model,Mathematics,Viterbi algorithm | Journal |
Volume | Issue | ISSN |
6.0 | 2 | 1066-5277 |
Citations | PageRank | References |
6 | 0.83 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L Yu | 1 | 7 | 1.17 |
| Temple F. Smith | 2 | 139 | 73.26 |