Review Article
Translational Biomedical Informatics in the Cloud: Present and Future
Table 1
Spectrum of translational bioinformatics activities.
| | Subfields | Research purpose | Data types | Informatics tools |
| | Bioinformatics | Sequencing
Structure analysis
Expression analysis
Phylogenetic analysis
Structure modeling | Sequence information
Microarray
Mass spectrum
SNP
Haplotypes | Pattern recognition [45–47]
Data mining [48–50]
Machine learning [47, 51, 52]
Visualization [53–55]
Automatic annotation [49, 55–58] |
| | Imaging informatics | Image feature identification
Image segmentation
Image reconstruction
Image annotation
Image indexing
Image visualization | DICOM
JPEG
TIFF
PNG
GIF
BMP | Content-based image retrieval [59, 60]
Natural language processing [61, 62] |
| | Clinical informatics | Clinical decision support
Clinical information access
Electronic patient record system
Disease reclassification | Clinical laboratory results
Physical examination
Symptoms and signs
Patient history
Prescriptions | Probabilistic decision-making [63]
Expert reasoning system [64–66]
Assessment and validation vocabularies [67, 68]
Text-parsing tools [69] |
| | Public health informatics | Tracking of infectious diseases
Assessment clinical interventions
Monitoring disease risk factors | PHCDM-based health information | Access control [70, 71]
Information security technology [37, 72]
Semantic and syntactic standards [73]
Structured data-collection techniques [74, 75] |
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