COPYRIGHT YEAR

2017

AUTHORS

Jonathan C. Badger, Peggy Peissig, Eric LaRose, Ross Kleiman, Ahmad P. Tafti

TITLE

Machine Learning-as-a-Service and Its Application to Medical Informatics

ABSTRACT

Machine learning as an advanced computational technology has been around for several years in discovering patterns from diverse biomedical data sources and providing excellent capabilities ranging from gene annotation to predictive phenotyping. However, machine learning strategies remain underused in small and medium-scale biomedical research labs where they have been collaboratively providing a reasonable amount of scientific knowledge. While most machine learning algorithms are complicated in code, theses labs and individual researchers could accomplish iterative data analysis using different machine learning techniques if they had access to highly available machine learning components and powerful computational infrastructures. In this contribution, we provide a comparison of several state-of-the-art Machine Learning-as-a-Service platforms along with their capabilities in medical informatics. In addition, we performed several analyses to examine the qualitative and quantitative attributes of two Machine Learning-as-a-Service environments namely “BigML” and “Algorithmia”.

How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

Download the RDF metadata as:   json-ld nt turtle xml License info


28 TRIPLES      24 PREDICATES      25 URIs      13 LITERALS

Subject Predicate Object
1 book-chapters:582bac15da857757e95f2eca805137bb sg:abstract Abstract Machine learning as an advanced computational technology has been around for several years in discovering patterns from diverse biomedical data sources and providing excellent capabilities ranging from gene annotation to predictive phenotyping. However, machine learning strategies remain underused in small and medium-scale biomedical research labs where they have been collaboratively providing a reasonable amount of scientific knowledge. While most machine learning algorithms are complicated in code, theses labs and individual researchers could accomplish iterative data analysis using different machine learning techniques if they had access to highly available machine learning components and powerful computational infrastructures. In this contribution, we provide a comparison of several state-of-the-art Machine Learning-as-a-Service platforms along with their capabilities in medical informatics. In addition, we performed several analyses to examine the qualitative and quantitative attributes of two Machine Learning-as-a-Service environments namely “BigML” and “Algorithmia”.
2 sg:abstractRights OpenAccess
3 sg:bibliographyRights Restricted
4 sg:bodyHtmlRights Restricted
5 sg:bodyPdfRights Restricted
6 sg:copyrightHolder Springer International Publishing AG
7 sg:copyrightYear 2017
8 sg:ddsId Chap15
9 sg:doi 10.1007/978-3-319-62416-7_15
10 sg:esmRights OpenAccess
11 sg:hasBook books:a81c146c04972590262fce2e97c2abe0
12 sg:hasBookEdition book-editions:ec86d64d174f148b14ef9475bf28e13a
13 sg:hasContribution contributions:35173b581bcb38215565398922a9e5a4
14 contributions:6117dba950f654f9285545ba67b9b6e1
15 contributions:9a077ec1c285838cce4e6e1723f04b35
16 contributions:a02799fd27b9bc1cfef6647388e37bd4
17 contributions:f62cf4bda4c328108fc608ef298b5982
18 sg:language En
19 sg:license http://scigraph.springernature.com/explorer/license/
20 sg:metadataRights OpenAccess
21 sg:pageFirst 206
22 sg:pageLast 219
23 sg:scigraphId 582bac15da857757e95f2eca805137bb
24 sg:title Machine Learning-as-a-Service and Its Application to Medical Informatics
25 sg:webpage https://link.springer.com/10.1007/978-3-319-62416-7_15
26 rdf:type sg:BookChapter
27 rdfs:label BookChapter: Machine Learning-as-a-Service and Its Application to Medical Informatics
28 owl:sameAs http://lod.springer.com/data/bookchapter/978-3-319-62416-7_15
HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular JSON format for linked data.

curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/book-chapters/582bac15da857757e95f2eca805137bb'

N-Triples is a line-based linked data format ideal for batch operations .

curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/book-chapters/582bac15da857757e95f2eca805137bb'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/book-chapters/582bac15da857757e95f2eca805137bb'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/book-chapters/582bac15da857757e95f2eca805137bb'






Preview window. Press ESC to close (or click here)


...