The Experts below are selected from a list of 5058960 Experts worldwide ranked by ideXlab platform
Dhundy Bastola - One of the best experts on this subject based on the ideXlab platform.
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An Integrated Approach to Recognize Potential Protective Effects of Culinary Herbs Against Chronic Diseases
Journal of Healthcare Informatics Research, 2019Co-Authors: Suganya Chandrababu, Dhundy BastolaAbstract:Secondary metabolites in plants have been of interest to humans for their wide variety of functions, including its use as dye, drugs, or perfumes. They are increasingly recognized as potential sources of new natural drugs and antibiotics. More recently, gut-associated microbes have been found to fulfill important functions in human health. However, our knowledge about the impact of secondary metabolites from culinary herbs on gut microbiome is limited. The present study was conducted to access the availability of computational resources relating to secondary metabolites and bioactive compounds in culinary herbs. A graph-based database HerbMicrobeDataBase (HMDB) was developed using Neo4j framework. It integrates knowledge from key biological entities associated in maintaining gut health and provides efficient storage/retrieval and graphical presentation of botanical, biochemical, and pharmacological data for culinary herbs and the human microbiome. We demonstrate the utility of this resource in understanding the molecular mechanism of metabolite production as well as their therapeutic or toxicological effects on gut microbes.
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graph model for the identification of multi target drug information for culinary herbs
7th International Work-Conference on Bioinformatics and Biomedical Engineering IWBBIO 2019, 2019Co-Authors: Suganya Chandrababu, Dhundy BastolaAbstract:Drug discovery strategies based on natural products are re-emerging as a promising approach. Due to its multi-target therapeutic properties, natural compounds in herbs produce greater levels of efficacy with fewer adverse effects and toxicity than monotherapies using synthetic compounds. However, the study of these medicinal herbs featuring multi-components and multi-targets requires an understanding of complex relationships, which is one of the fundamental goals in the discovery of drugs using natural products. Relational database systems such as the MySQL and Oracle store data in multiple tables, which are less efficient when data such as the one from natural compounds contain many relationships requiring several joins of large tables. Recently, there has been a noticeable shift in paradigm to NoSQL databases, especially graph databases, which was developed to natively represent complex high throughput dynamic relations. In this paper, we demonstrate the feasibility of using a graph-based database to capture the dynamic biological relationships of natural plant products by comparing the performance of MySQL and one of the most widely used NoSQL graph databases called Neo4j. Using this approach we have developed a graph database HerbMicrobeDB (HbMDB), and integrated herbal drug information, herb-targets, metabolic pathways, gut-microbial interactions and bacterial-genome information, from several existing resources. This NoSQL database contains 1,975,863 nodes, 3,548,314 properties and 2,511,747 edges. While probing the database and testing complex query execution performance of MySQL versus Neo4j, the latter outperformed MySQL and exhibited a very fast response for complex queries, whereas MySQL displayed latent or unfinished responses for complex queries with multiple-join statements. We discuss information convergence of pharmacochemistry, bioactivities, drug targets, and interaction networks for 24 culinary herbs and human gut microbiome. It is seen that all the herbs studied contain compounds capable of targeting a minimum of 55 enzymes and a maximum of 250 enzymes involved in biochemical pathways important in disease pathology.
Suganya Chandrababu - One of the best experts on this subject based on the ideXlab platform.
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An Integrated Approach to Recognize Potential Protective Effects of Culinary Herbs Against Chronic Diseases
Journal of Healthcare Informatics Research, 2019Co-Authors: Suganya Chandrababu, Dhundy BastolaAbstract:Secondary metabolites in plants have been of interest to humans for their wide variety of functions, including its use as dye, drugs, or perfumes. They are increasingly recognized as potential sources of new natural drugs and antibiotics. More recently, gut-associated microbes have been found to fulfill important functions in human health. However, our knowledge about the impact of secondary metabolites from culinary herbs on gut microbiome is limited. The present study was conducted to access the availability of computational resources relating to secondary metabolites and bioactive compounds in culinary herbs. A graph-based database HerbMicrobeDataBase (HMDB) was developed using Neo4j framework. It integrates knowledge from key biological entities associated in maintaining gut health and provides efficient storage/retrieval and graphical presentation of botanical, biochemical, and pharmacological data for culinary herbs and the human microbiome. We demonstrate the utility of this resource in understanding the molecular mechanism of metabolite production as well as their therapeutic or toxicological effects on gut microbes.
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graph model for the identification of multi target drug information for culinary herbs
7th International Work-Conference on Bioinformatics and Biomedical Engineering IWBBIO 2019, 2019Co-Authors: Suganya Chandrababu, Dhundy BastolaAbstract:Drug discovery strategies based on natural products are re-emerging as a promising approach. Due to its multi-target therapeutic properties, natural compounds in herbs produce greater levels of efficacy with fewer adverse effects and toxicity than monotherapies using synthetic compounds. However, the study of these medicinal herbs featuring multi-components and multi-targets requires an understanding of complex relationships, which is one of the fundamental goals in the discovery of drugs using natural products. Relational database systems such as the MySQL and Oracle store data in multiple tables, which are less efficient when data such as the one from natural compounds contain many relationships requiring several joins of large tables. Recently, there has been a noticeable shift in paradigm to NoSQL databases, especially graph databases, which was developed to natively represent complex high throughput dynamic relations. In this paper, we demonstrate the feasibility of using a graph-based database to capture the dynamic biological relationships of natural plant products by comparing the performance of MySQL and one of the most widely used NoSQL graph databases called Neo4j. Using this approach we have developed a graph database HerbMicrobeDB (HbMDB), and integrated herbal drug information, herb-targets, metabolic pathways, gut-microbial interactions and bacterial-genome information, from several existing resources. This NoSQL database contains 1,975,863 nodes, 3,548,314 properties and 2,511,747 edges. While probing the database and testing complex query execution performance of MySQL versus Neo4j, the latter outperformed MySQL and exhibited a very fast response for complex queries, whereas MySQL displayed latent or unfinished responses for complex queries with multiple-join statements. We discuss information convergence of pharmacochemistry, bioactivities, drug targets, and interaction networks for 24 culinary herbs and human gut microbiome. It is seen that all the herbs studied contain compounds capable of targeting a minimum of 55 enzymes and a maximum of 250 enzymes involved in biochemical pathways important in disease pathology.
Tojo Mathew - One of the best experts on this subject based on the ideXlab platform.
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a cypher query based nosql data mining on protein datasets using Neo4j graph database
International Conference on Advanced Computing, 2017Co-Authors: C I Johnpaul, Tojo MathewAbstract:Graph data analysis is one of the upcoming methodologies in various niches of computer science. Traditionally for storing, retrieving and experimenting test data, researchers start with mysql database which is more approachable and easier to build their test experimentation platform. These test bed mysql databases will store data in the form of rows and columns, over which various SQL queries are performed. At times when the structure and size of dataset changed, these traditional mysql databases become inefficient in storing and retrieving of data. When the structure of dataset changes from row-column to graph representation, mysql database based querying and analysis become inefficient. The internal representation of data is changed to key-value pairs, more often the data in an unstructured format, which prompted the researchers to think about other databases which can achieve faster retrieval and mining over the dataset. This paper explores the approach of NoSql query design and analysis of different datasets, particularly a proteome-protein dataset over a renowned graph database, Neo4j. The mode of experiments involve the evaluation of NoSql query execution on datasets vary in the number of nodes and relationships between them. It also emphasises the process of mining large graphs with meaningful queries based on a NoSql Query language called Cypher.
Yuhan Sun - One of the best experts on this subject based on the ideXlab platform.
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answering location aware graph reachability queries on geosocial data
International Conference on Data Engineering, 2017Co-Authors: Mohamed Sarwat, Yuhan SunAbstract:Thanks to the wide spread use of mobile and wearable devices, popular social networks, e.g., Facebook, prompts users to add spatial attributes to social entities, e.g., check-ins, traveling posts, and geotagged photos, leading to what is known as, The GeoSocial Graph. In such graph, usersmay issue a Reachability Query with Spatial Range Predicate (abbr. RangeReach). RangeReach finds whether an input vertex can reach any spatial vertex that lies within an input spatial range. The paper proposes GEOREACH, an approach that adds spatial data awareness to a graph database management system. GEOREACH allows efficient execution of RangeReach queries, yet without compromising a lot on the overall system scalability. Experiments based on system implementation inside Neo4j prove that GEOREACH exhibits up to two orders of magnitude better query performance and up to four times less storage than the state-of-the-art spatial and reachability indexing approaches.
C I Johnpaul - One of the best experts on this subject based on the ideXlab platform.
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a cypher query based nosql data mining on protein datasets using Neo4j graph database
International Conference on Advanced Computing, 2017Co-Authors: C I Johnpaul, Tojo MathewAbstract:Graph data analysis is one of the upcoming methodologies in various niches of computer science. Traditionally for storing, retrieving and experimenting test data, researchers start with mysql database which is more approachable and easier to build their test experimentation platform. These test bed mysql databases will store data in the form of rows and columns, over which various SQL queries are performed. At times when the structure and size of dataset changed, these traditional mysql databases become inefficient in storing and retrieving of data. When the structure of dataset changes from row-column to graph representation, mysql database based querying and analysis become inefficient. The internal representation of data is changed to key-value pairs, more often the data in an unstructured format, which prompted the researchers to think about other databases which can achieve faster retrieval and mining over the dataset. This paper explores the approach of NoSql query design and analysis of different datasets, particularly a proteome-protein dataset over a renowned graph database, Neo4j. The mode of experiments involve the evaluation of NoSql query execution on datasets vary in the number of nodes and relationships between them. It also emphasises the process of mining large graphs with meaningful queries based on a NoSql Query language called Cypher.
