1 article(s) from Munteanu, Cristian R.
Figure 1: Screenshot illustrating free text search finding ontology annotated database entries (e.g. protocol...
Figure 2: Top level substance API documentation. The “GET /substance” call is used to retrieve or search a li...
Figure 3: Screenshot showing a nanomaterial entry (a gold nanoparticle with the name G15.AC) and its componen...
Figure 4: Experimental data JSON example.
Figure 5: Physico-chemical and toxicity data from the NanoWiki data set.
Figure 6: Compound, substance and study search API documentation.
Figure 7: Outline of the data model: Substances are characterised by their “composition” and are identified b...
Figure 8: Data upload web page of the database system showing support for two file formats.
Figure 9: Bundle API documentation at http://enanomapper.github.io/API. A bundle is a REST resource, allowing...
Figure 10: Screenshot of the bundle view with the Protein Corona data set. In addition to the Substance API, w...
Figure 11: Physicochemical data for multi-walled carbon nanotubes. The screenshot illustrates the data model a...
Figure 12: Toxicity data for multi-walled carbon nanotubes. The repeated dose toxicity (inhalation) is shown i...
Figure 13: Screenshot showing the results of a chemical similarity query (octyl amine, SMILES CCCCCCCCN) with ...
Figure 14: Screenshot showing query results in the NanoWiki data set for particle sizes between 50 and 60 nm. ...
Figure 15: Pie chart created with d3.js and ambit.js in a web page showing that the NanoWiki and Protein Coron...
Figure 16: API call in ambit.js code.
Figure 17: Histogram of nanomaterial sizes created with d3.js and ambit.js.
Figure 18: Scatter plot of nanomaterial zeta potentials against the nanomaterial sizes, also created with d3.j...
Figure 19: Screenshot of the Jaqpot Quattro modelling web services API, compatible with the eNanoMapper API. A...
Figure 20: Conjoiner API: modelling-oriented information can be extracted from bundles of experimental data. D...
Figure 21: Example of a PMML document.
Figure 22: JPDI-compliant web services can be seamlessly incorporated into the eNanoMapper framework. The clie...
Figure 23: Algorithm API that allows to consume as well as register new algorithms (following the JPDI specifi...
Figure 24: A JPDI request for training.
Figure 25: A model returned by JPDI service in JSON format.
Figure 26: An example of a JSON prediction request.
Figure 27: Screenshot of the descriptors calculated with quantum mechanics MOPAC web service.