Introducing JSON

Friesen, Jeff

doi:10.1007/978-1-4842-4330-5_7

Cited by 8 publications

(6 citation statements)

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“…In the set of technologies, it is necessary to formalize the format for receiving and transmitting data, as well as the possibility of transferring data to the database. These can be generally accepted and common formats for storing and transmitting JSON or XML data, however, the choice of formats is not limited and may change based on speed or convenience reasons for storing data [10], [11].…”

Section: A Selection Of Software Components For Algorithmic Supportmentioning

confidence: 99%

Algorithmic support of a personal virtual assistant for automating the processing of client requests

Dobratulin¹,

Nezhurina²

2022

Preprint

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This article describes creating algorithmic support for the functioning of a personal virtual assistant, which allows automating the processing of customer requests. The study aims to reduce errors and processing time for a client request in business systems -text chats or voice channels using a text transcription system. The results of the development of algorithmic support and an assessment of the quality of work on synthetic data presented.

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Section: A Selection Of Software Components For Algorithmic Supportmentioning

confidence: 99%

Algorithmic support of a personal virtual assistant for automating the processing of client requests

Dobratulin¹,

Nezhurina²

2022

Preprint

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“…There are many ways to structure data, from graph databases [3], delimited text format, to json files [4]. As evidence synthesists, it is often not possible to select a database format that will be optimal for all use cases.…”

Section: Why Databases Are Integral To Evidence Synthesesmentioning

confidence: 99%

“…3 Africa Centre for Evidence, University of Johannesburg, Johannesburg, South Africa. 4 School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK. 5 Norwegian Institute for Nature Research, Torgarden, Postbox 5685, 7485 Trondheim, Norway.…”

Section: Supplementary Informationmentioning

confidence: 99%

Novel tools and methods for designing and wrangling multifunctional, machine-readable evidence synthesis databases

2021

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One of the most important steps in the process of conducting a systematic review or map is data extraction and the production of a database of coding, metadata and study data. There are many ways to structure these data, but to date, no guidelines or standards have been produced for the evidence synthesis community to support their production. Furthermore, there is little adoption of easily machine-readable, readily reusable and adaptable databases: these databases would be easier to translate into different formats by review authors, for example for tabulation, visualisation and analysis, and also by readers of the review/map. As a result, it is common for systematic review and map authors to produce bespoke, complex data structures that, although typically provided digitally, require considerable efforts to understand, verify and reuse. Here, we report on an analysis of systematic reviews and maps published by the Collaboration for Environmental Evidence, and discuss major issues that hamper machine readability and data reuse or verification. We highlight different justifications for the alternative data formats found: condensed databases; long databases; and wide databases. We describe these challenges in the context of data science principles that can support curation and publication of machine-readable, Open Data. We then go on to make recommendations to review and map authors on how to plan and structure their data, and we provide a suite of novel R-based functions to support efficient and reliable translation of databases between formats that are useful for presentation (condensed, human readable tables), filtering and visualisation (wide databases), and analysis (long databases). We hope that our recommendations for adoption of standard practices in database formatting, and the tools necessary to rapidly move between formats will provide a step-change in transparency and replicability of Open Data in evidence synthesis.

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“…After that, the Natural Language Processing (NLP) python module was employed to perform sentence tokenization into unique sentences with the tokenizer "nltk.data.load english.pickle" [24] [25]. All tokenized sentences were then written into the database using json.dumps function in JSON module [26] [27].…”

Section: (I)mentioning

confidence: 99%

NRF2 drug repurposing using a question-answer artificial intelligence system

Mickael

Pajares

Enache

et al. 2019

Preprint

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Drug repurposing represents an innovative approach to reduce the drug development timeline. Text mining using artificial intelligence methods offers great potential in the context of drug repurposing. Here, we present a question-answer artificial intelligence (QAAI) system that is capable of repurposing drug compounds. Our system employs a Google semantic AI universal encoder to compute the sentence embedding of an imposed text question in relation to publications stored in our RedBrain JSON database. Sentences similarity is calculated using a sorting function to identify drug compounds. We demonstrate our system's ability to predict new indications for already existing drugs. Activation of the NRF2 pathway seems critical for enhancing several diseases prognosis. We experimentally validated the prediction for the lipoxygenase inhibitor drug zileuton as a modulator of the NRF2 pathway in vitro, with potential applications to reduce macrophage M1 phenotype and ROS production. This novel computational method provides a new approach to reposition of known drugs in order to treat neurodegenerative diseases. Github for the database and the code can be downloaded from https://gist.github.com/micheledw/5a165b44345d45105d715340b88c756b

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Introducing JSON

Cited by 8 publications

References 0 publications

Algorithmic support of a personal virtual assistant for automating the processing of client requests

Algorithmic support of a personal virtual assistant for automating the processing of client requests

Novel tools and methods for designing and wrangling multifunctional, machine-readable evidence synthesis databases

NRF2 drug repurposing using a question-answer artificial intelligence system

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