scholarly journals KnowMore: an automated knowledge discovery tool for the FAIR SPARC datasets

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1132
Author(s):  
Ryan Quey ◽  
Matthew A. Schiefer ◽  
Anmol Kiran ◽  
Bhavesh Patel
Keyword(s):  
Knowledge Discovery ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Discovery Process ◽  
Data Practices ◽  
Speed Up ◽  
Automated Knowledge ◽  
Data Ecosystem ◽  
Automated Discovery ◽  
Grand Prize

Background: This manuscript provides the methods and outcomes of KnowMore, the Grand Prize winning automated knowledge discovery tool developed by our team during the 2021 NIH SPARC FAIR Data Codeathon. The National Institutes of Health Stimulating Peripheral Activity to Relieve Conditions (NIH SPARC) program generates rich datasets from neuromodulation researches, curated according to the Findable, Accessible, Interoperable, and Reusable (FAIR) SPARC data standards. Currently, the process of simultaneously comparing and analyzing multiple SPARC datasets is tedious because it requires investigating each dataset of interest individually and downloading all of them to conduct cross-analyses. It is crucial to enhance this process to enable rapid discoveries across SPARC datasets. Methods: To fill this need, we created KnowMore, a tool integrated into the SPARC Portal that only requires the user to select their datasets of interest to launch an automated discovery process. KnowMore uses several SPARC resources (Pennsieve, o²S²PARC, SciCrunch, protocols.io, Biolucida), data science methods, and machine learning algorithms in the back end to generate various visualizations in the front end intended to help the user identify potential similarities, differences, and relations across the datasets. These visualizations can lead to a new discovery, new hypothesis, or simply guide the user to the next logical step in their discovery process. Results: The outcome of this project is a SPARC portal-ready code architecture that helps researchers to use SPARC datasets more efficiently and fully leverages their FAIR characteristics. The tool has been built and documented such that more data analysis methods and visualization items could be easily added. Conclusions: The potential for automated discoveries from SPARC datasets is huge given the unique SPARC data ecosystem promoting FAIR data practices, and KnowMore has only demonstrated a small highlight of what could be achieved to speed up discoveries from SPARC datasets.

scholarly journals KnowMore: An Automated Knowledge Discovery Tool for the FAIR SPARC Datasets

2021 ◽  
Author(s):  
Ryan Quey ◽  
Matthew A. Schiefer ◽  
Anmol Kiran ◽  
Bhavesh Patel
Keyword(s):  
Knowledge Discovery ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Discovery Process ◽  
Data Practices ◽  
Speed Up ◽  
Automated Knowledge ◽  
Data Ecosystem ◽  
Automated Discovery ◽  
Grand Prize

This manuscript provides the methods and outcomes of KnowMore, the Grand Prize winning automated knowledge discovery tool developed by our team during the 2021 NIH SPARC FAIR Data Codeathon. The NIH SPARC program generates rich datasets from neuromodulation researches, curated according to the Findable, Accessible, Interoperable, and Reusable (FAIR) SPARC data standards. These datasets are publicly available through the SPARC Data Portal at sparc.science. Currently, the process of simultaneously comparing and analyzing multiple SPARC datasets is tedious because it requires investigating each dataset of interest individually and downloading all of them to conduct cross-analyses. It is crucial to enhance this process to enable rapid discoveries across SPARC datasets. To fill this need, we created KnowMore, a tool integrated into the SPARC Portal that only requires the user to select their datasets of interest to launch an automated discovery process. KnowMore uses several SPARC resources (Pennsieve, o2S2PARC, SciCrunch, protocols.io, Biolucida), data science methods, and Machine Learning algorithms in the back end to generate various visualizations in the front end intended to help the user identify potential similarities, differences, and relations across the datasets. These visualizations can lead to a new discovery, new hypothesis, or simply guide the user to the next logical step in their discovery process. The outcome of this project is a SPARC portal-ready code architecture that helps researchers to use SPARC datasets more efficiently and fully leverages their FAIR characteristics. The tool has been built and documented such that more data analysis methods and visualization items could be easily added. The potential for automated discoveries from SPARC datasets is huge given the unique SPARC data ecosystem promoting FAIR data practices, and KnowMore has only demonstrated a small highlight of what could be achieved to speed up discoveries from SPARC datasets.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

The Structuration of Digital Ecosystem, Privacy, and Big Data Intelligence

2018 ◽  
Vol 62 (10) ◽  
pp. 1319-1337 ◽  
Author(s):  
Yong Jin Park ◽  
Jae Eun Chung ◽  
Dong Hee Shin
Keyword(s):  
Digital Media ◽  
Personal Data ◽  
Policy Implications ◽  
Surveillance Systems ◽  
Data Practices ◽  
Digital Ecosystem ◽  
Digital Surveillance ◽  
The Status ◽  
Data Surveillance ◽  
Data Ecosystem

This study presents a conceptual model of understanding algorithmic digital surveillance systems, borrowing insight from Giddens, who proposed the notion of structuration as social practices deriving from the intersection between structure and agents. We argue that the status of privacy, or lack of it, is a product of these interactions, of which the personal data practices and related interests constitute the reproduction of a data ecosystem. We trace the process of data production and consumption, dissecting the interactive dynamics between digital media producers (personal data users) and users (personal data producers). Inadequacies, limits, and social and policy implications of data surveillance and its algorithmic reproduction of identities are discussed.

scholarly journals Knowledge discovery in Chinese herbal medicine: a machine learning perspective

2021 ◽  
Vol 336 ◽  
pp. 06024
Author(s):  
Nan Liang ◽  
Qing Liang ◽  
Fenglei Ji
Keyword(s):  
Machine Learning ◽  
Herbal Medicine ◽  
Knowledge Discovery ◽  
Chinese Herbal Medicine ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Current Status ◽  
Network Pharmacology ◽  
Valuable Insight ◽  
Chinese Herbal

Traditional Chinese Medicine (TCM) has attracted more and more attention due to its remarkable effects on treating diseases, and Chinese herbal medicine (CHM) is an important partition of TCM, rich in natural active ingredients. Researchers are trying multiple analytical methods to dig out more valuable information about CHM and reveal the principle of TCM. Machine learning is playing an important role in the studies. Knowledge discovery of CHM using machine learning mainly includes quality control of CHM, network pharmacology in CHM, and medical prescriptions composed by CHM, aiming to understand TCM better, provide more efficiency methods in the production of CHM and find novel treatment of disease not curable nowadays. In this paper, we summarized the basic idea of frequently used classification and clustering machine learning algorithms, introduced pre-processing algorithms commonly used to simplify and accelerate machine learning procedure, presented current status of machine learning algorithms’ applications in knowledge discovery of CHM, discussed challenges and future trends of machine learning’s application in CHM. It is believed that the paper provides a valuable insight for the starters trying to apply machine learning in the study of CHM and catch up the recent status of related researches.

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