Characterisation of FAD-family folds using a machine learning approach

Flavin adenine dinucleotide (FAD) and its derivatives play a crucial role in biological processes. They are major organic cofactors and electron carriers in both enzymatic activities and biochemical pathways. We have analysed the relationships between sequence and structure of FAD-containing proteins using a machine learning approach. Decision trees were generated using the C4.5 algorithm as a means of automatically generating rules from biological databases (TOPS, CATH and PDB). These rules were then used as background knowledge for an ILP system to characterise the four different classes of FAD-family folds classified in Dym and Eisenberg (2001). These FAD-family folds are: glutathione reductase (GR), ferredoxin reductase (FR), p-cresol methylhydroxylase (PCMH) and pyruvate oxidase (PO). Each FADfamily was characterised by a set of rules. The “knowledge patterns” generated from this approach are a set of rules containing conserved sequence motifs, secondary structure sequence elements and folding information. Every rule was then verified using statistical evaluation on the measured significance of each rule. We show that this machine learning approach is capable of learning and discovering interesting patterns from large biological databases and can generate “knowledge patterns” that characterise the FADcontaining proteins, and at the same time classify these proteins into four different families

Knowledge discovering for document classification using tree matching in Texpros

This dissertation describes a knowledge-based system for classifying documents based upon the layout structure and conceptual information extracted from the content of the document. The spatial elements in a document are laid out in rectangular blocks which are represented by nodes in an ordered labelled tree, called the layout structure tree (L-S Tree). Each leaf node of a L-S Tree points to its corresponding block content. A knowledge Acquisition Tool (KAT) is devised to create a Document Sample Tree from L-S Tree, in which each of its leaves contains a node content conceptually describing its corresponding block content. Then, applying generalization rules, the KAT performs the inductive learning from Document Sample Trees of a type and generates fewer number of Document Type Trees to represent its type. A testing document is classified if a Document Type Tree is discovered as a substructure of the L-S Tree of the testing document; and then the exact format of the testing document can be found by matching the L-S Tree with the Document Sample Trees of the classified document type. The Document Sample Trees and Document Type Trees are called Structural Knowledge Base (SKB). The tree discovering and matching processes involve computing the edit distance and the degree of conceptual closeness between the SKB trees and the L-S Tree of a testing document by using pattern matching and discovering toolkits. Our experimental results demonstrate that many office documents can be classified correctly using the proposed approach

FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases

In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed. It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Identification of Interaction Patterns and Classification with Applications to Microarray Data

Emerging patterns represent a class of interaction structures which has been recently proposed as a tool in data mining. In this paper, a new and more general definition refering to underlying probabilities is proposed. The defined interaction patterns carry information about the relevance of combinations of variables for distinguishing between classes. Since they are formally quite similar to the leaves of a classification tree, we propose a fast and simple method which is based on the CART algorithm to find the corresponding empirical patterns in data sets. In simulations, it can be shown that the method is quite effective in identifying patterns. In addition, the detected patterns can be used to define new variables for classification. Thus, we propose a simple scheme to use the patterns to improve the performance of classification procedures. The method may also be seen as a scheme to improve the performance of CARTs concerning the identification of interaction patterns as well as the accuracy of prediction