Links and Paths through Life Sciences Data Sources

Lacroix, Zoé; Murthy, Hyma; Naumann, Felix; Raschid, Louiqa

doi:10.1007/978-3-540-24745-6_14

Cited by 25 publications

(12 citation statements)

References 8 publications

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“…In BioNavigation 25,26,29 , a query is defined as a regular expression LR(E) over the alphabet V of scientific entities. Thus an expression e is defined recursively as follows:…”

Section: Bionavigationmentioning

confidence: 99%

Path-Based Systems to Guide Scientists in the Maze of Biological Data Sources

Cohen-Boulakia

Davidson

Froidevaux

et al. 2006

J. Bioinform. Comput. Biol.

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Fueled by novel technologies capable of producing massive amounts of data for a single experiment, scientists are faced with an explosion of information which must be rapidly analyzed and combined with other data to form hypotheses and create knowledge. Today, numerous biological questions can be answered without entering a wet lab. Scientific protocols designed to answer these questions can be run entirely on a computer.Biological resources are often complementary, focused on different objects and reflecting various experts' points of view. Exploiting the richness and diversity of these resources is crucial for scientists. However, with the increase of resources, scientists have to face the problem of selecting sources and tools when interpreting their data.In this paper, we analyze the way in which biologists express and implement scientific protocols, and we identify the requirements for a system which can guide scientists in constructing protocols to answer new biological questions. We present two such systems, BioNavigation and BioGuide dedicated to help scientists select resources by following suitable paths within the growing network of interconnected biological resources. Fueled by novel technologies capable of producing massive amounts of data for a single experiment, scientists are faced with an explosion of information which must be rapidly analyzed and combined with other data to form hypotheses and create knowledge. Today, numerous biological questions can be answered without entering a wet lab. Scientific protocols designed to answer these questions can be run entirely on a computer.Biological resources are often complementary, focused on different objects and reflecting various experts' points of view. Exploiting the richness and diversity of these resources is crucial for scientists. However, with the increase of resources, scientists have to face the problem of selecting sources and tools when interpreting their data.In this paper, we analyze the way in which biologists express and implement scientific protocols, and we identify the requirements for a system which can guide scientists in constructing protocols to answer new biological questions. We present two such systems, BioNavigation and BioGuide dedicated to help scientists select resources by following suitable paths within the growing network of interconnected biological resources.

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“…In BioNavigation 25,26,29 , a query is defined as a regular expression LR(E) over the alphabet V of scientific entities. Thus an expression e is defined recursively as follows:…”

Section: Bionavigationmentioning

confidence: 99%

Path-Based Systems to Guide Scientists in the Maze of Biological Data Sources

Cohen-Boulakia

Davidson

Froidevaux

et al. 2006

J. Bioinform. Comput. Biol.

Self Cite

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“…Important applications include the Semantic Web [3,4], social network analysis [27], biological networks [34], and several others. The standard model for a graph database is as an edge-labeled directed graph [12,30]: nodes represent objects and a labeled edge between nodes represents the fact that a particular type of relationship holds between these two objects.…”

Section: Introductionmentioning

confidence: 99%

Regular Queries on Graph Databases

2016

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Graph databases are currently one of the most popular paradigms for storing data. One of the key conceptual differences between graph and relational databases is the focus on navigational queries that ask whether some nodes are connected by paths satisfying certain restrictions. This focus has driven the definition of several different query languages and the subsequent study of their fundamental properties.We define the graph query language of Regular Queries, which is a natural extension of unions of conjunctive 2-way regular path queries (UC2RPQs) and unions of conjunctive nested 2-way regular path queries (UCN2RPQs). Regular queries allow expressing complex regular patterns between nodes. We formalize regular queries as nonrecursive Datalog programs with transitive closure rules. This language has been previously considered, but its algorithmic properties are not well understood.Our main contribution is to show elementary tight bounds for the containment problem for regular queries. Specifically, we show that this problem is 2Expspace-complete. For all extensions of regular queries known to date, the containment problem turns out to be nonelementary. Together with the fact that evaluating regular queries is not harder than evaluating UCN2RPQs, our results show that regular queries achieve a good balance between expressiveness and complexity, and constitute a well-behaved class that deserves further investigation.

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“…Researching links involve many steps: link-based object classification (categorizes objects primarily based on links and attributes) [7], object kind prediction (predicts object types supported attributes, links, and objects joined to it) [8], link kind prediction (predicts the aim of the link supported the objects involved) [9], link existence prediction (predicts the existence of a link) [10], link cardinality estimation (predicting the amount of links (and objects reached) to an object) [11], object reconciliation (determining whether or not 2 objects are the same supported their links) [12], cluster detection (predicting if an object set belongs together) [13], subgraph detection (discovering sub-graphs inside networks) [14], and data mining (mining for information concerning data) [15], [16]. Other samples of mining social networks are link prediction, namely exploitation the options intrinsic of the present model of a social network to model future connections inside the network.…”

Section: Introductionmentioning

confidence: 99%

Efficient Data Retrieval using Combine Approach of SOM and K-Mean Clustering

Sharma¹

2016

IJCA

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Emergence of recent techniques for scientific knowledge collection has resulted in large scale accumulation of information relating various fields. Typical info querying ways are inadequate to extract helpful data from huge knowledge banks. Cluster analysis is one of the key knowledge analysis way and the k-means clustering algorithm is widely used for several data mining applications. The analysis of the cancer data set with the k mean and then applying with the Som. Many ways are planned within the literature for improving the performance with the kmeans clustering formula. This paper proposes a technique for creating knowledge retrieval more practical and efficient using som with K mean clustering technique, So as to get better clustering with reduced quality.

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Links and Paths through Life Sciences Data Sources

Cited by 25 publications

References 8 publications

Path-Based Systems to Guide Scientists in the Maze of Biological Data Sources

Path-Based Systems to Guide Scientists in the Maze of Biological Data Sources

Regular Queries on Graph Databases

Efficient Data Retrieval using Combine Approach of SOM and K-Mean Clustering

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