A filtering algorithm for efficient retrieving of DNA sequence

DNA sequence similarity search is an important task in computational biology applications. Similarity search procedure is executed by an alignment process between query and targeted sequences. An optimal alignment process based on the dynamic programming algorithms has shown to have O(n m) time and space complexity. Heuristics algorithms can process a fast DNA sequence alignment, but generate low comparison sensitivity. The biologists frequently demand for optimal comparison result so that the perfect structure of living beings evolution can be constructed. This task becomes more complex and challenging as the sizes of public sequence databases get very large and are increasing exponentially each year. The aim of this study is to develop a filtering algorithm in order to reduce the iteration of dynamic programming process and therefore an efficient process of retrieving a set of similar DNA sequences in database can be made. The algorithm filtered the expected irrelevant DNA sequences in database from being computed for dynamic programming based optimal alignment process. An automaton-based algorithm is used to develop the filtering process proposed. A set of random patterns is generated from query sequence are placed in automaton machine before exact matching and scoring process is performed. Extensive experiments have been carried out on several parameters and the results show that the developed filtering algorithm removed the unrelated targeted sequences from being aligned with query sequenc

SEARCHING HETEROGENEOUS DOCUMENT IMAGE COLLECTIONS

A decrease in data storage costs and widespread use of scanning devices has led to massive quantities of scanned digital documents in corporations, organizations, and governments around the world. Automatically processing these large heterogeneous collections can be difficult due to considerable variation in resolution, quality, font, layout, noise, and content. In order to make this data available to a wide audience, methods for efficient retrieval and analysis from large collections of document images remain an open and important area of research. In this proposal, we present research in three areas that augment the current state of the art in the retrieval and analysis of large heterogeneous document image collections. First, we explore an efficient approach to document image retrieval, which allows users to perform retrieval against large image collections in a query-by-example manner. Our approach is compared to text retrieval of OCR on a collection of 7 million document images collected from lawsuits against tobacco companies. Next, we present research in document verification and change detection, where one may want to quickly determine if two document images contain any differences (document verification) and if so, to determine precisely what and where changes have occurred (change detection). A motivating example is legal contracts, where scanned images are often e-mailed back and forth and small changes can have severe ramifications. Finally, approaches useful for exploiting the biometric properties of handwriting in order to perform writer identification and retrieval in document images are examined

Document Ranking by Layout Relevance

This paper describes the development of a new document ranking system based on layout similarity. The user has a need represented by a set of ”wanted ” documents, and the system ranks documents in the collection according to this need. Rather than performing complete document analysis, the system extracts text lines, and models layouts as relationships between pairs of these lines. This paper explores three novel feature sets to support scoring in large document collections. First, pairs of lines are used to form quadrilaterals, which are represented by their turning functions. A non-Euclidean distance is used to measure similarity. Second, the quadrilaterals are represented by 5D Euclidean vectors, and third, each line is represented by a 5D Euclidean vector. We compare the classification performance and computation speed of these three feature sets using a large database of diverse documents including forms, academic papers and handwritten pages in English and Arabic. The approach using quadrilaterals and turning functions produces slightly better results, but the approach using vectors to represent text lines is much faster for large document databases. 1