Ligand-Based Virtual Screening Using Bayesian Inference Network and Reweighted Fragments

Ahmed, Ali; Abdo, Ammar; Salim, Naomie

doi:10.1100/2012/410914

Cited by 19 publications

(24 citation statements)

References 26 publications

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“…where  is definitional domain of g. The objective function of maximum posterior estimation can be gotten by (8):…”

Section: A Fuzzy-precise Bayesian Networkmentioning

confidence: 99%

Model and Algorithm Design for Cargo Shipping Safety Based on Fuzzy- Precise Bayesian Network

Wang¹,

Zhang²

2014

JNW

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The shippers have the requests for the accessibility and economics of logistics on the premise of cargo safety, and the cargo shipping safety is an important part of logistics safety. Thus, it is quite essential to monitor the cargo shipping safety. The data is difficult to obtain in the research for cargo shipping safety, thus it cannot cover all states of influencing factors during modeling process of Bayesian network. In order to solve this problem, the computation process of Bayesian network was improved in this paper, and the Fuzzy-precise Bayesian network was obtained. The Fuzzy-precise Bayesian network was applied to monitor cargo shipping safety. According to the multi-field coupling theory, the index system influencing the cargo shipping safety was established, the GeNIe software was used to conduct the inference of Bayesian network. The inference model was utilized for conducting the safety monitoring of specific cargo on a bulk cargo ship of a company in Hainan province, and the effectiveness and availability of this model was examined. This paper provides guidance for safety production of enterprises' cargo shipping.

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“…where  is definitional domain of g. The objective function of maximum posterior estimation can be gotten by (8):…”

Section: A Fuzzy-precise Bayesian Networkmentioning

confidence: 99%

Model and Algorithm Design for Cargo Shipping Safety Based on Fuzzy- Precise Bayesian Network

Wang¹,

Zhang²

2014

JNW

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“…Bayesian Networks (Niedermayer, 2008) are increasingly being used in a variety of application areas like searching (Teevan, 2011), (Acid et al, 2003), (Callan, 2009), Bioinformatics (Ahmed et al, 2012), and many others. An important subclass of Bayesian Networks is the Bayesian Inference Networks (BIN) (Turtle, 1991) that have been employed in various applications (Teevan, 2011), (Ma et al, 2006), (Abdo et al, 2011).…”

Section: Literature Overviewmentioning

confidence: 99%

SerfSIN: Search Engines Results' Refinement using a Sense-driven Inference Network

Makris

Plegas

Tzimas

et al. 2013

Proceedings of the 9th International Conference on Web Information Systems and Technologies

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Α novel framework is presented for performing re-ranking in the search results of a Web search engine, incorporating user judgments as registered in their selection of relevant documents. The proposed scheme combines smoothly techniques from the area of Inference Networks with text processing techniques exploiting semantic information, and is instantiated to a fully functional prototype at present leading to a reranking whose quality outperforms significantly the initial ranking. The innovative idea is the use of a probabilistic network based to the senses of the documents. When the user selects a document, the belief of the network to the senses of the selected document is raised up and the documents that contain these senses are ranked higher. Also we present an implemented prototype that supports three different Web search engines (and it can be extended to support many more), while extensive experiments in the ClueWeb09 dataset using the TREC's 2009, 2010 and 2011 Web Tracks' data depict the improvement in search performance that the proposed approach attains.

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“…The study showed that BINRF model had improved in the retrieval effectiveness of the similarity-based screening method. Moreover, this method has a very significant advantage in boosting the performance when there is a high degree of structural heterogeneity among the active structures that is being searched [15].…”

Section: Tss Performance In Various Conditionsmentioning

confidence: 99%

“…In the recent work of [15], the use of the Bayesian Inference Network (BIN) in ligand-based virtual screening was an alternative to improve the effectiveness of the approach. Later on, in order to improve the retrieval effectiveness of BIN, fragment reweighting was applied in the set of active reference structure.…”

Section: Tss Performance In Various Conditionsmentioning

confidence: 99%

The Effect of Adding Indirect Relationship to Turbo Similarity Searching

Malim¹,

Pei-Chia²,

Al-Laila³

et al. 2016

PHARMSCI

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Background:Turbo Similarity Searching (TSS) has been proved as one of the effective and simple searching method in Cheminformatics. Emerging from the conventional similarity searching, TSS depended on the concept of fusion where relationship between the target being sought and the compound in the database are indirect. Previous works has looked at only one level of indirect relationship and indicates that there are further potential that more levels of such relationship be added to TSS to increase its ability to recover more actives. Hence, in this work, we aimed to investigate the impact of the indirect relationship on TSS. Method:This study has further investigated the enhancement of TSS using additional layers of indirect relationship and fusion process. We implemented TSS by adding another layer of fusion between the target and database compound. Results:The experiments with MDDR database showed that the proposed new strategy described in this paper provide a way of enhancing the effectiveness of the TSS process in chemical databases. The experiments also showed that the increases in performance are particularly better when the sought actives are structurally diverse. Conclusion:We may conclude that the additional layers do increase the recall of TSS. Hence, the new TSS strategy could be used as an alternative to the old TSS.

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Ligand-Based Virtual Screening Using Bayesian Inference Network and Reweighted Fragments

Cited by 19 publications

References 26 publications

Model and Algorithm Design for Cargo Shipping Safety Based on Fuzzy- Precise Bayesian Network

Model and Algorithm Design for Cargo Shipping Safety Based on Fuzzy- Precise Bayesian Network

SerfSIN: Search Engines Results' Refinement using a Sense-driven Inference Network

The Effect of Adding Indirect Relationship to Turbo Similarity Searching

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