Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
Self-organizing neural networks to support the discovery of DNA-binding motifs
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Motif discovery by monotone scores
Discrete Applied Mathematics
Artificial Intelligence in Medicine
An upper bound on the hardness of exact matrix based motif discovery
Journal of Discrete Algorithms
Shuffling biological sequences with motif constraints
Journal of Discrete Algorithms
| Hi-index | 12.05 |
Objective: This paper presents an algorithm for the solution of the motif discovery problem (MDP). Methods and materials: Motif discovery problem can be considered in two cases: motifs with insertions/deletions, and motifs without insertions/deletions. The first group motifs can be found by stochastic and approximated methods. The second group can be found by using stochastic and approximated methods, but also deterministic method. We proved that the second group motifs can be found with a deterministic algorithm, and so, it can be said that the second motifs finding is a P-type problem as proved in this paper. Results and conclusions: An algorithm was proposed in this paper for motif discovery problem. The proposed algorithm finds all motifs which are occurred in the sequence at least two times, and it also finds motifs of various sizes. Due to this case, this algorithm is regarded as Automatic Exact Motif Discovery Algorithm. All motifs of different sizes can be found with this algorithm, and this case was proven in this paper. It shown that automatic exact motif discovery is a P-type problem in this paper. The application of the proposed algorithm has been shown that this algorithm is superior to MEME, MEME3, Motif Sampler, WEEDER, CONSENSUS, AlignACE.