Comparative study of probability models for compound similarity searching

The quality of a chemical retrieval system heavily depends on its molecular similarity function which returns a similarity measurement between the target compound and each molecule in the collection. Compounds are sorted according to their similarity values with the query and those with high ranks a...

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Main Authors: Salim, Naomie, Mulyadi, Mercy Trinovianti
Format: Conference or Workshop Item
Language:English
Published: 2005
Subjects:
Online Access:http://eprints.utm.my/10086/1/NaomieSalim2005_ComparativeStudyofProbabilityModels.pdf
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author Salim, Naomie
Mulyadi, Mercy Trinovianti
author_facet Salim, Naomie
Mulyadi, Mercy Trinovianti
author_sort Salim, Naomie
collection ePrints
description The quality of a chemical retrieval system heavily depends on its molecular similarity function which returns a similarity measurement between the target compound and each molecule in the collection. Compounds are sorted according to their similarity values with the query and those with high ranks are returned to the users. Most current chemical retrieval systems use the vector space model for similarity calculation. In this paper, the use of probability of relevance for compound retrieval is explored. It reports on the effectiveness of the probability model for compound similarity searching by using Binary Independence Model and Binary Dependence Madel on two different databases. The result based on fusion of queries for both models is also discussed. The results show that in all cases, Binary Independence Retrieval model performed better than Binary Dependence model. It is also found that fusion does not give better results than the un-fused queries.
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spelling utm.eprints-100862017-06-12T06:49:06Z http://eprints.utm.my/10086/ Comparative study of probability models for compound similarity searching Salim, Naomie Mulyadi, Mercy Trinovianti QA75 Electronic computers. Computer science The quality of a chemical retrieval system heavily depends on its molecular similarity function which returns a similarity measurement between the target compound and each molecule in the collection. Compounds are sorted according to their similarity values with the query and those with high ranks are returned to the users. Most current chemical retrieval systems use the vector space model for similarity calculation. In this paper, the use of probability of relevance for compound retrieval is explored. It reports on the effectiveness of the probability model for compound similarity searching by using Binary Independence Model and Binary Dependence Madel on two different databases. The result based on fusion of queries for both models is also discussed. The results show that in all cases, Binary Independence Retrieval model performed better than Binary Dependence model. It is also found that fusion does not give better results than the un-fused queries. 2005-12 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/10086/1/NaomieSalim2005_ComparativeStudyofProbabilityModels.pdf Salim, Naomie and Mulyadi, Mercy Trinovianti (2005) Comparative study of probability models for compound similarity searching. In: International Conference on Information Technology in Asia, 12-15 December 2005, Hilton Hotel, Kuching, Sarawak. http://dblp2.uni-trier.de/rec/bibtex/conf/cita/SalimM05
spellingShingle QA75 Electronic computers. Computer science
Salim, Naomie
Mulyadi, Mercy Trinovianti
Comparative study of probability models for compound similarity searching
title Comparative study of probability models for compound similarity searching
title_full Comparative study of probability models for compound similarity searching
title_fullStr Comparative study of probability models for compound similarity searching
title_full_unstemmed Comparative study of probability models for compound similarity searching
title_short Comparative study of probability models for compound similarity searching
title_sort comparative study of probability models for compound similarity searching
topic QA75 Electronic computers. Computer science
url http://eprints.utm.my/10086/1/NaomieSalim2005_ComparativeStudyofProbabilityModels.pdf
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