Graph Model for the Identification of Multi-target Drug Information for Culinary Herbs

Chandrababu, Suganya; Bastola, Dhundy

doi:10.1007/978-3-030-17938-0_44

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…They provide alternatives to error prone and time consuming exercises to gather results from multiple sources, which may involve one or many of the tasks like cross referencing across web services, manual browsing, performing federated queries across databases, etc. Many such projects exist ( [19,20,15,21,22,23] ) and, like CompoundDB4j, provide a comprehensive data-centered integration to provide methods to collate and analyze data sources of disparate provenance and storage formats. Deriving from the above, we have developed an ordered network of wellconnected data built on a single graph database to discover synergies between the following entities: Compounds (drugs), Targets, Activities, Assays, Proteins, Pathways, Diseases, Patent Records, Drug Interactions, Metabolites, and Structure Alerts.…”

Section: Introductionmentioning

confidence: 99%

CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases

Murali

Königs

Deekshitula

et al. 2020

Molecular Informatics

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Computational approaches to analyze various drug/ compound centered analysis often present a need to map attributes from multiple drug databases. In this study, we provide a Neo4j repository that integrates two of the most prominent open source drug databases, DrugBank and ChEMBL, with a goal of establishing an integrated data visualization and analysis tool for drug discovery studies. The drugs present in DrugBank are mapped to their counterparts in ChEMBL. The integration of these resources and the harmonization using knowledge graph serialization using Neo4j lead to identification of relationships between drugs and other related features that are otherwise spread across two different resources. A common data format, a prerequisite to populate the Neo4j database, enables users to identify new relationships central to drug discovery research, like Drug Target Interactions (DTI). The resource is freely available at: https://github.com/ambf0632/CompoundDB4j

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Section: Introductionmentioning

confidence: 99%

CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases

Murali

Königs

Deekshitula

et al. 2020

Molecular Informatics

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A ranking based multi-view learning method for positive and unlabeled graph classification

Liu

Che

Zhong

et al. 2021

IEEE Trans. Knowl. Data Eng.

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A Network-based Analysis of Ingredients Lists in Nutrient Profile Systems

Flores

Cooper

2021

Curr Res Nutr Food Sci

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It is well-established that diseases can be prevented or mitigated through dietary intervention, yet proper nutrition is one aspect consumers struggle to manage. Recent studies have shown that there are barriers to understanding the nutrition facts component of a food label which can be linked to dietary choices. In this work, we demonstrate reproducibility and replicability of a network-based method for automating the analysis of ingredients on a food product label en masse using the Open Food Facts Database and the USDA FoodData Central Branded Foods database in February 2020. Our results, which analyze the co-occurrence of 72,754 ingredients across show some consensus in labeling across FALCPA-regulated ingredients in food product labels across databases but highlight potential areas for discrepancy in consumer understanding and labeling practices for terms not subject to strict regulations. The key findings or contributions of this work include the provision of a reproducible method for quantifying the ingredients of packaged food in the United States across two nutritional profiling systems, and have identified 17 total ingredients that appear in the top 20 most co-occurring ingredients for both databases examined. We compare how of 8 FALCPA-regulated ingredients are represented in ingredients lists versus a common, but non-FALCPA regulated ingredient (corn), to demonstrate how one could examine differences between ingredient labeling between products. These findings suggest more research is needed in developing information systems to increase information available for consumers.

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Graph Model for the Identification of Multi-target Drug Information for Culinary Herbs

Cited by 3 publications

References 17 publications

CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases

CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases

A ranking based multi-view learning method for positive and unlabeled graph classification

A Network-based Analysis of Ingredients Lists in Nutrient Profile Systems

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