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Novel drug target identification for the treatment of dementia using multi-relational association mining.

Nguyen TP, Priami C, Caberlotto L - Sci Rep (2015)

Bottom Line: Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance.Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor.Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies.

View Article: PubMed Central - PubMed

Affiliation: 1] The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy [2] Life Sciences Research Unit, University of Luxembourg, 162 A, avenue de la Faïencerie, L-1511 Luxembourg.

ABSTRACT
Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia.

No MeSH data available.


Related in: MedlinePlus

Example of a MRD in table form (left) and in graph form (right).The entity types ‘pathway’, ‘protein’, and ‘degree’ correspond to different blocks in the graph and the entities of each type correspond to different nodes. The table ‘Reactome_Pathway’ defines the pathway description. The join table ‘Of_Pathway’ defines a many-to-many relationship between the entity types ‘pathway’ and ‘protein’ and the table ‘Centrality’ defines an one-to-many relationship between entities ‘protein’ and ‘degree’. Two entities are linked with an edge if they co-occur in a same tuple.
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f3: Example of a MRD in table form (left) and in graph form (right).The entity types ‘pathway’, ‘protein’, and ‘degree’ correspond to different blocks in the graph and the entities of each type correspond to different nodes. The table ‘Reactome_Pathway’ defines the pathway description. The join table ‘Of_Pathway’ defines a many-to-many relationship between the entity types ‘pathway’ and ‘protein’ and the table ‘Centrality’ defines an one-to-many relationship between entities ‘protein’ and ‘degree’. Two entities are linked with an edge if they co-occur in a same tuple.

Mentions: The extracted data from GO, i2d, InterPro and Reactome are represented as relational tables in the Microsoft SQL server management system. Later one-to-many or many-to-many relationships among the tables were established. For example, many proteins may have the same degree centrality (one-to-many relationship) and on the other side a protein may belong to many Reactome pathways and one Reactome pathway has many proteins involved (many-to-many relationship). Figure 3 illustrates an example of extracted data in a multi-relational table form where ‘pathway’, ‘protein’, and ‘degree’ are three entity types, shown at the left-hand side of the figure. There is a relationship type between ‘pathway’ and ‘protein’, specifying the pathways that the proteins take part in, and between ‘protein’ and ‘degree’, specifying the degree centrality corresponding to a protein. The first relationship type is a many-to-many while the second is one- to-many. Note that the heterogeneity in the data makes the use of classical AM unsuitable in mining multiple relational data and underlines the importance of using an MRAM algorithm for the identification of the DTs.


Novel drug target identification for the treatment of dementia using multi-relational association mining.

Nguyen TP, Priami C, Caberlotto L - Sci Rep (2015)

Example of a MRD in table form (left) and in graph form (right).The entity types ‘pathway’, ‘protein’, and ‘degree’ correspond to different blocks in the graph and the entities of each type correspond to different nodes. The table ‘Reactome_Pathway’ defines the pathway description. The join table ‘Of_Pathway’ defines a many-to-many relationship between the entity types ‘pathway’ and ‘protein’ and the table ‘Centrality’ defines an one-to-many relationship between entities ‘protein’ and ‘degree’. Two entities are linked with an edge if they co-occur in a same tuple.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495601&req=5

f3: Example of a MRD in table form (left) and in graph form (right).The entity types ‘pathway’, ‘protein’, and ‘degree’ correspond to different blocks in the graph and the entities of each type correspond to different nodes. The table ‘Reactome_Pathway’ defines the pathway description. The join table ‘Of_Pathway’ defines a many-to-many relationship between the entity types ‘pathway’ and ‘protein’ and the table ‘Centrality’ defines an one-to-many relationship between entities ‘protein’ and ‘degree’. Two entities are linked with an edge if they co-occur in a same tuple.
Mentions: The extracted data from GO, i2d, InterPro and Reactome are represented as relational tables in the Microsoft SQL server management system. Later one-to-many or many-to-many relationships among the tables were established. For example, many proteins may have the same degree centrality (one-to-many relationship) and on the other side a protein may belong to many Reactome pathways and one Reactome pathway has many proteins involved (many-to-many relationship). Figure 3 illustrates an example of extracted data in a multi-relational table form where ‘pathway’, ‘protein’, and ‘degree’ are three entity types, shown at the left-hand side of the figure. There is a relationship type between ‘pathway’ and ‘protein’, specifying the pathways that the proteins take part in, and between ‘protein’ and ‘degree’, specifying the degree centrality corresponding to a protein. The first relationship type is a many-to-many while the second is one- to-many. Note that the heterogeneity in the data makes the use of classical AM unsuitable in mining multiple relational data and underlines the importance of using an MRAM algorithm for the identification of the DTs.

Bottom Line: Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance.Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor.Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies.

View Article: PubMed Central - PubMed

Affiliation: 1] The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI), Piazza Manifattura 1, 38068, Rovereto, Italy [2] Life Sciences Research Unit, University of Luxembourg, 162 A, avenue de la Faïencerie, L-1511 Luxembourg.

ABSTRACT
Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia.

No MeSH data available.


Related in: MedlinePlus