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COMPUTATIONAL INFERENCE TECHNIQUE FOR MINING STRUCTURED MOTIFS
Published 2012Subjects: “…Suffix tree…”
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CONCEPTUAL KNOWLEDGE MODEL FOR IMPROVING TERM SIMILARITY IN RETRIEVAL OF WEB DOCUMENTS
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Page will reload when a filter is selected or excluded.- DNA Binding Site 1 results 1
- Multiple document sources 1 results 1
- One of the major challenges in bioinformatics is the development of efficient computational tools for mining patterns. Structured motifs, like DNA binding sites in organisms with peculiarities in their genomic sequence like malaria parasite, Plasmodium falciparum have not been mined by existing structured motifs extraction tools. There is a need to develop faster computational tools to mine these DNA binding sites which are viable drug targets. This work was aimed at developing an algorithm for mining structured motifs in the genome of P. falciparum. The Gene Enrichment Motif Searching (GEMS) method for mining simple motifs was modified by incorporating the time efficient implementation of the suffix tree data structure with suffix links. This enables an improved searching speed, while adding an optimized position-weight matrix computation using the hypergeometric-based scoring function. This algorithm, Suffix Tree Gene Enrichment Motif Searching (STGEMS) was implemented in C programming language on Linux platform. An empirical evaluation of the sensitivity of STGEMS was conducted by comparing the similarity check mechanism of the GEMS algorithm for mining simple motifs with that used in another popular algorithm for extracting structured motifs, a Multi-Objective Genetic Algorithm Motif Discovery (MOGAMOD). The output of STGEMS algorithm was validated by comparing the motifs discovered with those obtained using biological experiments. A further validation was done by applying the STGEMS and GEMS algorithm to selected metabolic pathways and the results were compared. The STGEMS algorithm was tested with four sets of genes from the intraerythrocytic development cycle of P. falciparum. The speed of execution was evaluated using three simple motif discovery tools: Expectation Maximization Motif Elicitation(MEME), Gene Enrichment Motif Search (GEMS), and WEEDER as well as two structured motif discovery tools: RISOTTO and EXMOTIF on four different gene sizes.The high sensitivity of STGEMS in mining structured motifs from sequences in P. falciparum was proven empirically by its ability to identify 91% of the motifs in the sequences while MOGAMOD could not identify any motif. This validated the high sensitivity of the similarity check mechanism employed, in contrast with that used in MOGAMOD. The STGEMS algorithm identified 90% of the binding sites in P. falciparum which were similar to those obtained in biological experiments. On the selected metabolic pathways, STGEMS discovered all the simple motifs identified by GEMS, in addition to the structured motifs which GEMS could not identify. The empirical runtimes of STGEMS, MEME, WEEDER, GEMS, RISOTTO and EXMOTIF were respectively 20, 35, 26, 25, 28, 30 seconds for 20,000 base pair (bp), 32, 43, 44, 45, 42, 40 seconds for 40,000 bp, 41, 55, 56, 55, 52, 50 seconds for 60,000 bp and 54, 68, 69, 65, 67, 61 seconds for 80,000 bp respectively. The proposition resulted in a linear asymptotic runtime of O(N) at each iteration of the algorithm. The suffix tree gene enrichment motif searching algorithm developed was time efficient and successful in mining structured motifs like DNA binding sites in Plasmodium 15 falciparum. This will aid a faster drug target discovery pipeline for the design of effective anti malaria drugs. 1 results 1
- Structured motifs 1 results 1
- Suffix tree 1 results 1
- Term similarity 1 results 1
- Terms Similarity (TS) in retrieval systems are based on lexical matching, which determines if query terms are useful and reflect the users’ information need in related domains. Existing works on TS use Term Frequency-Inverse Document Frequency (TF-IDF) to determine the occurrence of terms in web documents (snippets) is incapable of capturing the problem of semantic language mismatch. This study was designed to develop a conceptual knowledge model to solve the problem of TS in web documents retrieval by amplifying structured semantic network in Multiple Document Sources (MDSs) to reduce mismatch in retrieval results. Four hundred and forty-two IS-A hierarchy concepts were extracted from Internet using a web ontology language. These hierarchies were structured in MDSs to determine similarities. The concepts were used to formulate queries with the addition of terms from knowledge domain. Suffix Tree Clustering (STC) was adapted to cluster, structure the web and reduce dimensionality of features. The IS-A hierarchy concept on parent and child relationship was incorporated into the STC to select the best cluster, consisting of 100 snippets, four web page counts and WordNet as MDSs. Similarity was estimated on Cosine, Euclidean and Radial Basis Function (RBF) on the TF-IDF. Based on STC, TF-IDF was modified to develop Concept Weighting (CW) estimation on snippets and web page count. Similarity was estimated between TF-IDF and developed Concept Weighting; Cosine and CW-Cosine, Euclidean and CW-Euclidean and RBF and CW-RBF. Semantic network (WordNetSimilarity) LIn’ measure was extended with PAth length of the taxonomy concept to develop LIPA. The LIPA was compared with other WordNetSimilarity distance measures: Jiang and Conrath (JCN) and Wu and Palmer (WUP) as well as LIn and PAth length separately. Concept Weighting and WordNetSimilarity scores were combined using machine learning techniques to leverage a robust semantic similarity score and accuracy measure using Mean Absolute Error (MAE). The RBF and CW-RBF generated inconsistent values (0.9 for null and zero snippets. Similarity estimation obtained on Cosine, Euclidean, CW-Cosine and CW-Euclidean were 0.881, 0.446, 0.950 and 0.964, respectively. The retrieved snippets removed irrelevant features and enhanced precisions. WordNetSimilarity JCN, WUP, LIn, PAth, and LIPA values were 0.868, 0.953, 0.995, 0.955 and 0.998, respectively. The WordNetSimilarity improved the semantic similarity of concepts. The Concept Weighting and WordNetSimilarity; CW-Cosine, CW-Euclidean, JCN, WUP, LIn, PAth, and LIPA were combined to generate similarity coefficient scores 0.941, 0.944, 0.661, 0.928, 0.996, 0.924 and 0.998, respectively. The MAE on Cosine, Euclidean, CW-Cosine and CW Euclidean were 0.058, 0.011, 0.014 and 0.009, respectively while for JCN, WUP, LIn, PAth, and LIPA were 0.022, 0.004, 0.022, 0.019 and 0.020, respectively. The accuracy of the combined similarity for JCN, WUP, LIn, PAth, CW-Cosine, CW-Euclidean and LIPA were 0.023, 0.050, 0.008, 0.011, 0.024, 0.015 and 0.009, respectively. The developed conceptual knowledge model improved retrieval of web documents with structured multiple document sources. This improved precision of information retrieval system and solved the problem of semantic language mismatch with robust similarity between the terms. 1 results 1
- Web ontology language 1 results 1
- WordNetSimilarity 1 results 1
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