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3 article(s) from Sarimveis, Haralambos

The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

Dimitra-Danai Varsou,
Arkaprava Banerjee,
Joyita Roy,
Kunal Roy,
Giannis Savvas,
Haralambos Sarimveis,
Ewelina Wyrzykowska,
Mateusz Balicki,
Tomasz Puzyn,
Georgia Melagraki,
Iseult Lynch and
Antreas Afantitis

Beilstein J. Nanotechnol. 2024, 15, 1536–1553, doi:10.3762/bjnano.15.121

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Figure 1: Schematic representation of a negatively charged uncoated spherical NM. The ZP corresponds to the e...

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Figure 2: Schematic workflow for the development of the stacked PLS and MLP q-RASPR models.

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Figure 3: Schematic representation of the individual and consensus models for the RR exercise. The five model...

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Published 29 Nov 2024

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Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning

Richard Liam Marchese Robinson,
Haralambos Sarimveis,
Philip Doganis,
Xiaodong Jia,
Marianna Kotzabasaki,
Christiana Gousiadou,
Stacey Lynn Harper and
Terry Wilkins

Beilstein J. Nanotechnol. 2021, 12, 1297–1325, doi:10.3762/bjnano.12.97

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Figure 1: The laboratory method used to obtain the raw counts data for the lethal and sub-lethal endpoints ev...

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Figure 2: The transformation of the raw biological counts data, illustrated here for excess lethality at 120 ...

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Figure 3: A summary of all modelling work carried out using the model development dataset reported herein. A ...

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Figure 4: Data augmentation using the “noised training set replication” paradigm. Top: a subset of the availa...

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Figure 5: Data augmentation using the “weighted alternative samples” paradigm, where the alternative samples ...

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Figure 6: Data augmentation using the “weighted alternative samples” paradigm, where the alternative samples ...

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Figure 7: Nested cross-validated balanced accuracy (BA) obtained with multiple descriptor Random Forest model...

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Figure 8: Nested cross-validated balanced accuracy (BA) obtained with multiple descriptor Random Forest model...

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Figure 9: The change in cross-validated balanced accuracy (BA), with results shown for individual folds, obta...

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Figure 10: Variable importance estimates for the five most important variables (highest mean importance values...

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Figure 11: Distributions of conditional variable importance estimates for the five most important variables (h...

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Published 29 Nov 2021

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The eNanoMapper database for nanomaterial safety information

Nina Jeliazkova,
Charalampos Chomenidis,
Philip Doganis,
Bengt Fadeel,
Roland Grafström,
Barry Hardy,
Janna Hastings,
Markus Hegi,
Vedrin Jeliazkov,
Nikolay Kochev,
Pekka Kohonen,
Cristian R. Munteanu,
Haralambos Sarimveis,
Bart Smeets,
Pantelis Sopasakis,
Georgia Tsiliki,
David Vorgrimmler and
Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

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Figure 1: Screenshot illustrating free text search finding ontology annotated database entries (e.g. protocol...

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Figure 2: Top level substance API documentation. The “GET /substance” call is used to retrieve or search a li...

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Figure 3: Screenshot showing a nanomaterial entry (a gold nanoparticle with the name G15.AC) and its componen...

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Figure 4: Experimental data JSON example.

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Figure 5: Physico-chemical and toxicity data from the NanoWiki data set.

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Figure 6: Compound, substance and study search API documentation.

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Figure 7: Outline of the data model: Substances are characterised by their “composition” and are identified b...

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Figure 8: Data upload web page of the database system showing support for two file formats.

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Figure 9: Bundle API documentation at http://enanomapper.github.io/API. A bundle is a REST resource, allowing...

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Figure 10: Screenshot of the bundle view with the Protein Corona data set. In addition to the Substance API, w...

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Figure 11: Physicochemical data for multi-walled carbon nanotubes. The screenshot illustrates the data model a...

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Figure 12: Toxicity data for multi-walled carbon nanotubes. The repeated dose toxicity (inhalation) is shown i...

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Figure 13: Screenshot showing the results of a chemical similarity query (octyl amine, SMILES CCCCCCCCN) with ...

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Figure 14: Screenshot showing query results in the NanoWiki data set for particle sizes between 50 and 60 nm. ...

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Figure 15: Pie chart created with d3.js and ambit.js in a web page showing that the NanoWiki and Protein Coron...

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Figure 16: API call in ambit.js code.

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Figure 17: Histogram of nanomaterial sizes created with d3.js and ambit.js.

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Figure 18: Scatter plot of nanomaterial zeta potentials against the nanomaterial sizes, also created with d3.j...

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Figure 19: Screenshot of the Jaqpot Quattro modelling web services API, compatible with the eNanoMapper API. A...

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Figure 20: Conjoiner API: modelling-oriented information can be extracted from bundles of experimental data. D...

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Figure 21: Example of a PMML document.

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Figure 22: JPDI-compliant web services can be seamlessly incorporated into the eNanoMapper framework. The clie...

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Figure 23: Algorithm API that allows to consume as well as register new algorithms (following the JPDI specifi...

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Figure 24: A JPDI request for training.

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Figure 25: A model returned by JPDI service in JSON format.

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Figure 26: An example of a JSON prediction request.

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Figure 27: Screenshot of the descriptors calculated with quantum mechanics MOPAC web service.

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Published 27 Jul 2015

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