Pattern Discovery Algorithm. Bioinformatics In The Study

More and More new technologies in bioinformatics would be developed to benefit human beings when the biology data grows faster. Bioinformatic resources are mainly DNA sequences and protein sequences. Motif finding has important consequences because motifs expresse lives' pattern. It's useful to find motifs from multiple biology sequences which help us master the principle hidden how genes express. I propose a new solution by using mixture model whose candidate set comes from sample sequence for motif finding, and it outperforms greedyEM algorithm. I propose a new algorithm for subtle motif finding, my efforts focus on diminishing pattern space by using family tree and diminishing frequencies that multiple subsequences compare with motif pattern by using filter sieve. At present, biologists have turned their eyes on multiple genes association from single gene which takes role in lives' character. Motifs in regulate region regulate gene transfer. I present a new algorithm which finds different association rules sets between two motif data resources which lead to respective special gene express pattern. Main idea is that FP-tree first is built, dictionary trees is educed from it which construct forests, at last the action of comparing differences between two forests is taken. I also propose a data mining algorithm for promoter motif association rules finding.