Titlebook: Data Integration in the Life Sciences; 13th International C S?ren Auer,Maria-Esther Vidal Conference proceedings 2019 Springer Nature Switz

反抗者

https://doi.org/10.1007/978-3-030-69992-5 even more complex. Popular tools for facilitating the daily routine for the clinical researchers are more often based on machine learning (ML) algorithms. Those tools might facilitate data management, data integration or even content classification. Besides commercial functionalities, there are man

FADE

Linkun Zhang,Yuxia Lei,Zhengyan Wangore than a collection of (digital) documents. The main reason is in the fact that the document is the principal form of communication and—since underlying data, software and other materials mostly remain unpublished—the fact that the scholarly article is, essentially, the only form used to communica

RAG

Linkun Zhang,Yuxia Lei,Zhengyan Wangong statistical tools can lead to the reporting of dubious correlations as significant results. Missing information from lab protocols or other metadata can make verification impossible. Especially with the advent of Big Data in the life sciences and the hereby-involved measurement of thousands of m

euphoria

Computer Communications and Networksased applications have increased considerably during the last decade. Particularly in Life Sciences, RDF datasets are utilized to represent diverse concepts, e.g., proteins, genes, mutations, diseases, drugs, and side effects. Albeit publicly accessible, the exploration of these RDF datasets require

狗舍

Miyuru Dayarathna,Toyotaro Suzumura In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches m

Fester

身體萌芽

https://doi.org/10.1007/978-3-319-54645-2iratory and other viral infections can be judged from blood samples; however, it has not yet been determined which genetic expression levels are predictive, in particular for the early transition states of the disease onset. For these reasons, we analyse the expression levels of infected and non-inf

Crepitus

Yueyu Dong,Fei Dai,Mingming Qin as billing and reimbursement, quality control, epidemiological studies, and cohort identification for clinical trials. The codes are based on standardized vocabularies. Diagnostics, for example, are frequently coded using the International Classification of Diseases (ICD), which is a taxonomy of di

AUGUR

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