Abstract—Protein structure determination and prediction has been a focal research subject in the field of bioinformatics due to the importance of protein structure in understanding the biological and chemical activities of organisms. The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time. A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results. However, prediction accuracies of these methods rarely exceed 70%.

The biological data is available in different formats and is comparatively more complex. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of biology. The research in bioinformatics has accumulated large amount of data. As the hardware technology advancing, the cost of storing is decreasing. The biological data is available in different formats and is comparatively more complex. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of biology. In the present research work, Open Reading Frame is Detected with the help of data mining. Various consensus Sequences are gathered and Cluster algorithm is applied based on local alignment score calculation. The sequences having greater score will be more similar and this is basis for applying clustering algorithm. Another algorithm which uses the clusters created by previous algorithm is created for detecting the percentage match of the consensus sequence with the entered DNA sequence which further results for the Detection of Open reading Frame of entered sequence Index Terms

Abstract:Existing research in association mining has focused mainly on how to expedite the search for frequently cooccurring groups of items in “shopping cart ” type of transactions; less attention has been paid to methods that exploit these frequent itemsets for prediction purposes. This paper contributes to the latter task by proposing a technique that uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. Using the recently proposed data structure of itemset trees (IT-trees), we obtain, in a computationally efficient manner, all rules whose antecedents contain at least one item from the incomplete shopping cart. Then, we combine these rules by uncertainty processing techniques, including the classical Bayesian decision theory and a new algorithm based on the Dempster-Shafer (DS) theory of evidence combination. Key Words: Frequent itemsets, itemset trees (IT-Tresss), uncertainty processing, Dempster-Shafer theory.