scholarly journals YummyData: providing high-quality open life science data

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Yasunori Yamamoto ◽  
Atsuko Yamaguchi ◽  
Andrea Splendiani
Keyword(s):  
Life Science ◽  
Science Data ◽  
High Quality

scholarly journals Identifiers.org: Compact Identifier services in the cloud

2020 ◽  
Author(s):  
Manuel Bernal-Llinares ◽  
Javier Ferrer-Gómez ◽  
Nick Juty ◽  
Carole Goble ◽  
Sarala M Wimalaratne ◽  
...  
Keyword(s):  
Life Science ◽  
Life Sciences ◽  
High Availability ◽  
Third Party ◽  
Low Latency ◽  
Cloud Environment ◽  
Science Data ◽  
High Quality ◽  
Front End ◽  
Readable Text

Abstract Motivation Since its launch in 2010, Identifiers.org has become an important tool for the annotation and cross-referencing of Life Science data. In 2016, we established the Compact Identifier (CID) scheme (prefix: accession) to generate globally unique identifiers for data resources using their locally assigned accession identifiers. Since then, we have developed and improved services to support the growing need to create, reference and resolve CIDs, in systems ranging from human readable text to cloud-based e-infrastructures, by providing high availability and low-latency cloud-based services, backed by a high-quality, manually curated resource. Results We describe a set of services that can be used to construct and resolve CIDs in Life Sciences and beyond. We have developed a new front end for accessing the Identifiers.org registry data and APIs to simplify integration of Identifiers.org CID services with third-party applications. We have also deployed the new Identifiers.org infrastructure in a commercial cloud environment, bringing our services closer to the data. Availabilityand implementation https://identifiers.org.

scholarly journals FCC – An automated rule-based processing tool for life science data

2013 ◽  
Vol 8 (1) ◽  
pp. 3 ◽  
Author(s):  
Simon Barkow-Oesterreicher ◽  
Can Türker ◽  
Christian Panse
Keyword(s):  
Life Science ◽  
Science Data ◽  
Rule Based

Refined JST Thesaurus Extended with Data from Other Open Life Science Data Sources

2017 ◽  
pp. 35-48 ◽  
Author(s):  
Tatsuya Kushida ◽  
Yuka Tateisi ◽  
Takeshi Masuda ◽  
Katsutaro Watanabe ◽  
Katsuji Matsumura ◽  
...  
Keyword(s):  
Life Science ◽  
Data Sources ◽  
Science Data

Web Services and the Semantic Web for Life Science Data

2017 ◽  
pp. 1-8
Author(s):  
Cartik R. Kothari ◽  
Mark D. Wilkinson
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Life Science ◽  
Science Data

Web Services and the Semantic Web for Life Science Data

2018 ◽  
pp. 4669-4677
Author(s):  
Cartik R. Kothari ◽  
Mark D. Wilkinson
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Life Science ◽  
Science Data

Ontologies and Life Science Data Management

2018 ◽  
pp. 2570-2574
Author(s):  
Robert Stevens ◽  
Phillip Lord
Keyword(s):  
Data Management ◽  
Life Science ◽  
Science Data

scholarly journals Human-readable and machine-readable Persistent Identifiers

2015 ◽  
Author(s):  
Martin Fenner
Keyword(s):  
Life Science ◽  
Fundamental Problem ◽  
Science Data ◽  
Important Paper ◽  
Simple Rules ◽  
Machine Readable

Yesterday Julie McMurry and co-authors published a preprint 10 Simple rules for design, provision, and reuse of persistent identifiers for life science data. This is an important paper trying to address a fundamental problem: how can we make persistent ...

scholarly journals Colon Cancer Biomarkers: Implications for Personalized Medicine

2020 ◽  
Vol 10 (4) ◽  
pp. 167
Author(s):  
Kenneth P.H. Pritzker
Keyword(s):  
Colon Cancer ◽  
Personalized Medicine ◽  
Site Selection ◽  
Clinical Utility ◽  
Strong Association ◽  
Quality Analysis ◽  
Science Data ◽  
High Quality ◽  
Colon Cancers ◽  
Genomic Probes

The heterogeneity of colon cancers and their reactions presents both a challenge and promise for personalized medicine. The challenge is to develop effective biologically personalized therapeutics guided by predictive and prognostic biomarkers. Presently, there are several classes of candidate biomarkers, including genomic probes, inhibitory RNAs, assays for immunity dysfunction and, not to be forgotten, specific histopathologic and histochemical features. To develop effective therapeutics, candidate biomarkers must be qualified and validated in comparable independent cohorts, no small undertaking. This process and subsequent deployment in clinical practice involves not only the strong association of the biomarker with the treatment but also careful attention to the prosaic aspects of representative tumor site selection, obtaining a fully adequate sample which is preserved and prepared to optimize high quality analysis. In the future, the clinical utility of biomarker analytical results will benefit from associated clinical and basic science data with the assistance of artificial intelligence techniques. By application of an individualized, selected suite of biomarkers, comprehensively interpreted, individualized, more effective and less toxic therapy for colon cancer will be enabled, thereby fulfilling the promise of personalized medicine.

scholarly journals RIKEN MetaDatabase

2018 ◽  
Vol 14 (1) ◽  
pp. 140-164 ◽  
Author(s):  
Norio Kobayashi ◽  
Satoshi Kume ◽  
Kai Lenz ◽  
Hiroshi Masuya
Keyword(s):  
Graphical User Interface ◽  
Life Science ◽  
Data Distribution ◽  
Image Databases ◽  
Technical Knowledge ◽  
Science Data ◽  
Description Framework ◽  
Database Platform ◽  
Resource Description ◽  
The Web

Recently, the number and heterogeneity of life science datasets published on the Web have increased significantly. However, biomedical scientists face numerous serious difficulties finding, using and publishing useful databases. To address these issues, the authors developed a Resource Description Framework-based database platform, called the RIKEN MetaDatabase (http://metadb.riken.jp), that allows biologists to develop, publish and integrate multiple databases easily. The platform manages the metadata of both research and individual data described using standardised vocabularies and ontologies, and has a simple browser-based graphical user interface to view data including tabular and graphical forms. The platform was released in April 2015, and 113 databases, including mammalian, plant, bioresource and image databases, with 26 ontologies have been published using this platform as of January 2017. This paper describes the technical knowledge obtained through the development and operation of the RIKEN MetaDatabase to accelerate life science data distribution.

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