Full text retrieval based on syntactic similarities

Teufel, Bernd; Schmidt, Stephanie

doi:10.1016/0306-4379(88)90027-0

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…A number of different approaches have been developed for determining the similarity between nodes in social networks that take into account various topological characteristics of the underlying graphs ( [50], [13]). For the problem of scaffold-hopping, the similarity between a pair of nodes is determined as a function of the intersection of their adjacency lists ( [54], [56]), which takes into account all two-edge paths connecting these nodes.…”

Section: Methods Based On Indirect Similaritymentioning

confidence: 99%

Trends in Chemical Graph Data Mining

Wale

Ning

Karypis

2010

Managing and Mining Graph Data

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Mining chemical compounds in silico has drawn increasing attention from both academia and pharmaceutical industry due to its effectiveness in aiding the drug discovery process. Since graphs are the natural representation for chemical compounds, most of the mining algorithms focus on mining chemical graphs. Chemical graph mining approaches have many applications in the drug discovery process that include structure-activity-relationship (SAR) model construction and bioactivity classification, similar compound search and retrieval from chemical compound database, target identification from phenotypic assays, etc. Solving such problems in silico through studying and mining chemical graphs can provide novel perspective to medicinal chemists, biologist and toxicologist. Moreover, since the large scale chemical graph mining is usually employed at the early stages of drug discovery, it has the potential to speed up the entire drug discovery process. In this chapter, we discuss various problems and algorithms related to mining chemical graphs and describe some of the state-of-the-art chemical graph mining methodologies and their applications.

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Section: Methods Based On Indirect Similaritymentioning

confidence: 99%

Trends in Chemical Graph Data Mining

Wale

Ning

Karypis

2010

Managing and Mining Graph Data

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show abstract

“…A number of different approaches have been developed for determining the similarity between nodes in social networks that take into account various topological characteristics of the underlying graphs [13,28]. In our work, we determine the similarity between a pair of nodes as a function of the intersection of their adjacency lists, which takes into account all two-edge paths connecting these nodes.…”

Section: Nearest-neighbor Graph-based Methodsmentioning

confidence: 99%

Methods for Effective Virtual Screening and Scaffold-Hopping in Chemical Compounds

Wale¹,

Karypis²,

Watson³

2007

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. AbstractMethods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods, which are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. Two sets of techniques are presented that capture these indirect similarities using approaches based on automatic relevance feedback and on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that many of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.

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“…First the published content corresponding to each network addresses of the initial set is obtained. On the one hand, the network media information acquisition link then selectively stores the initial network address information subject content in an Internet information database [259][260][261], according to a series of content-heavy sentence mechanisms. On the other hand, the network media information acquisition link further extracts embedded hyperlink network addresses of the acquired information.…”

Section: Acquisition Process Of Network Media Informationmentioning

confidence: 99%

Survey on cyberspace security

Zhang

Han²,

Lai

et al. 2015

Sci. China Inf. Sci.

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Along with the rapid development and wide application of information technology, human society has entered the information era. In this era, people live and work in cyberspace. Cyberspace is the collection of all information systems; it is the information environment for human survival. Therefore, it is necessary to ensure the security of cyberspace. This paper gives a comprehensive introduction to research and development in this field, with a description of existing problems and some currently active research topics in the areas of cyberspace itself, cyberspace security, cryptography, network security, information system security and information content security.

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Full text retrieval based on syntactic similarities

Cited by 16 publications

References 9 publications

Trends in Chemical Graph Data Mining

Trends in Chemical Graph Data Mining

Methods for Effective Virtual Screening and Scaffold-Hopping in Chemical Compounds

Survey on cyberspace security

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