MOGAMOD: Multi-objective genetic algorithm for motif discovery Export

@article{citeulike:2744250, abstract = {We propose an efficient method using multi-objective genetic algorithm (MOGAMOD) to discover optimal motifs in sequential data. The main advantage of our approach is that a large number of tradeoff (i.e., nondominated) motifs can be obtained by a single run with respect to conflicting objectives: similarity, motif length and support maximization. To the best of our knowledge, this is the first effort in this direction. MOGAMOD can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. By analyzing the obtained optimal motifs, the decision maker can understand the tradeoff between the objectives. We compare MOGAMOD with the three well-known motif discovery methods, AlignACE, MEME and Weeder. Experimental results on real data set extracted from TRANSFAC database demonstrate that the proposed method exhibits good performance over the other methods in terms of accuracy and runtime.}, author = {Kaya, Mehmet}, citeulike-article-id = {2744250}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.eswa.2007.11.008}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0957417407005301}, citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6V03-4R53W74-M/1/0d7fd9a98b12b2f9e63a3ed9dd2c08b0}, doi = {10.1016/j.eswa.2007.11.008}, journal = {Expert Systems with Applications}, keywords = {algorithm, genetic}, posted-at = {2008-05-02 07:35:23}, priority = {2}, title = {MOGAMOD: Multi-objective genetic algorithm for motif discovery}, url = {http://dx.doi.org/10.1016/j.eswa.2007.11.008}, volume = {In Press, Corrected Proof} }

TY - JOUR ID - citeulike:2744250 L3 - citeulike-article-id:2744250 N2 - We propose an efficient method using multi-objective genetic algorithm (MOGAMOD) to discover optimal motifs in sequential data. The main advantage of our approach is that a large number of tradeoff (i.e., nondominated) motifs can be obtained by a single run with respect to conflicting objectives: similarity, motif length and support maximization. To the best of our knowledge, this is the first effort in this direction. MOGAMOD can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. By analyzing the obtained optimal motifs, the decision maker can understand the tradeoff between the objectives. We compare MOGAMOD with the three well-known motif discovery methods, AlignACE, MEME and Weeder. Experimental results on real data set extracted from TRANSFAC database demonstrate that the proposed method exhibits good performance over the other methods in terms of accuracy and runtime. JF - Expert Systems with Applications TI - MOGAMOD: Multi-objective genetic algorithm for motif discovery VL - In Press, Corrected Proof KW - algorithm KW - genetic AU - Kaya, Mehmet UR - http://dx.doi.org/10.1016/j.eswa.2007.11.008 ER -