Regulatory motifs are short patterns of nucleotides, usually 5-20 bp long, found common in the promoter region of set of co-expressed genes. Identification of these motifs gives insight into the regulatory mechanism of genes, as they control the expression or regulation of a group of genes involved in a similar cellular function. Motif discovery algorithms aim to discover these common patterns, which may present in either strand of the DNA double helix. Identification of DNA motifs is complex due to mutations, which make them weekly conserved patterns. This book describes two intelligent computing algorithms for discovering regulatory motifs. First algorithm, MotifMiner, is a table driven greedy algorithm. Even though it could identify meaningful motifs from the test dataset, due to greedy approach it may fall into local optimum. The second algorithm, AISMOTIF, is an artificial immune system based pattern discovery algorithm. The major advantage of this algorithm is its ability to generate all possible motifs in the input sequences simultaneously in reasonable time. This book is intended to research scholars in the areas of pattern matching and computational biology.
The author received Ph.D. in Computer Science from Jamia Hamdard University,India. Her research interests are Data Mining, Algorithm Design and Bioinformatics. She has published many research papers in referred journals. Currently, she is Assistant Professor in the Department of Computer Science, Jamia Hamdard University, New Delhi, India