Mining DNA microarray data using a novel approach based on graph theory

Río, Gabriel del; Bartley, Theodore; Del-Rio, Heberto; Rao, Rammohan V.; Jin, Kui; Greenberg, David A.; Eshoo, Mark W.; Bredesen, Dale E.

doi:10.1016/s0014-5793(01)03165-9

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…Recent large-scale studies of metabolic [15], transcriptomic [17] and proteomic [16,31] networks, based on analyses of experimental data, have found that their topologies belong within the class of scale-free networks.…”

Section: Resultsmentioning

confidence: 99%

“…metabolic [15], proteomic [16], and transcriptomic [17] networks) have found that their connectivities followed the power-law distribution, specifying that the probability of any node connecting to "k" other nodes is proportional to 1/k n . These networks are classified as scale-free networks and are in direct contrast to the bell-shaped distributions seen in random networks [15].…”

Section: Introductionmentioning

confidence: 99%

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Content-rich biological network constructed by mining PubMed abstracts

2004

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Background: The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Content-rich biological network constructed by mining PubMed abstracts

2004

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“…This theory was recently validated in protein networks in yeast cells where the most highly connected proteins were of vital importance for survival [3]. Similar findings were made in DNA microarray studies of yeast cells and in a rat model of ischemia [4,5]. Although the latter study only comprised a limited number of apoptosis-related genes it raised the question as to whether connectivity might also be used to identify key genes in global gene expression studies of human disease.…”

Section: Introductionmentioning

confidence: 65%

Connectivity can be used to identify key genes in DNA microarray data: a study based on gene expression in nasal polyps before and after treatment with glucocorticoids

Benson

Hov

Clancy

et al. 2007

Acta Oto-Laryngologica

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In all, 166 genes were differentially expressed; 39 of these were previously defined as inflammatory and considered important for nasal polyposis. The connectivity of all differentially expressed genes was analysed using the PubGene literature co-citation network. Seventy-four of the 166 genes were connected to other genes. By contrast, the average number of connected genes among 100 sets of 166 randomly chosen genes was 31.5. A small number of the differentially expressed genes were highly connected, while most genes had few or no connections. This indicated a scale-free network. The most connected gene was interleukin-8, an inflammatory gene of known importance for nasal polyposis. Twenty-eight of the 74 connected genes were inflammatory (38%), compared with 11 of the 92 unconnected genes (12%), p < 0.0001. Since most evidence suggests that nasal polyps are inflammatory in their nature, this supports the hypothesis that connected genes have more disease relevance than unconnected genes.

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“…We used the Dijkstra algorithm for tracing the shortest path between two vertices [21]. Hence, every vertex p is assigned a betweenness value obtained by counting the number of times each node is traversed in this process.…”

Section: Methodsmentioning

confidence: 99%

Improved prediction of critical residues for protein function based on network and phylogenetic analyses

2005

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Background: Phylogenetic approaches are commonly used to predict which amino acid residues are critical to the function of a given protein. However, such approaches display inherent limitations, such as the requirement for identification of multiple homologues of the protein under consideration. Therefore, complementary or alternative approaches for the prediction of critical residues would be desirable. Network analyses have been used in the modelling of many complex biological systems, but only very recently have they been used to predict critical residues from a protein's three-dimensional structure. Here we compare a couple of phylogenetic approaches to several different network-based methods for the prediction of critical residues, and show that a combination of one phylogenetic method and one network-based method is superior to other methods previously employed.

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Mining DNA microarray data using a novel approach based on graph theory

Cited by 15 publications

References 15 publications

Content-rich biological network constructed by mining PubMed abstracts

Content-rich biological network constructed by mining PubMed abstracts

Connectivity can be used to identify key genes in DNA microarray data: a study based on gene expression in nasal polyps before and after treatment with glucocorticoids

Improved prediction of critical residues for protein function based on network and phylogenetic analyses

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