2012
Gütlein, Martin; Karwath, Andreas; Kramer, Stefan
CheS-Mapper - Chemical Space Mapping and Visualization in 3D Journal Article
In: J. Cheminformatics, vol. 4, pp. 7, 2012.
Abstract | Links | BibTeX | Tags: cheminformatics, clustering, dimensionality reduction, QSAR, visualization
@article{gutlein2012,
title = {CheS-Mapper - Chemical Space Mapping and Visualization in 3D},
author = {Martin Gütlein and Andreas Karwath and Stefan Kramer},
url = {http://dx.doi.org/10.1186/1758-2946-4-7},
doi = {10.1186/1758-2946-4-7},
year = {2012},
date = {2012-03-17},
journal = {J. Cheminformatics},
volume = {4},
pages = {7},
abstract = {Analyzing chemical datasets is a challenging task for scientific researchers in the field of chemoinformatics. It is important, yet difficult to understand the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. To that respect, visualization tools can help to better comprehend the underlying correlations. Our recently developed 3D molecular viewer CheS-Mapper (Chemical Space Mapper) divides large datasets into clusters of similar compounds and consequently arranges them in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kind of features, like structural fragments as well as quantitative chemical descriptors. These features can be highlighted within CheS-Mapper, which aids the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. As a final function, the tool can also be used to select and export specific subsets of a given dataset for further analysis.
},
keywords = {cheminformatics, clustering, dimensionality reduction, QSAR, visualization},
pubstate = {published},
tppubtype = {article}
}
2009
Schulz, Hannes; Kersting, Kristian; Karwath, Andreas
ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries Conference
Inductive Logic Programming, 19th International Conference, ILP 2009, Springer-Verlag Berlin Heidelberg Springer Verlag, Berlin Heidelberg, Germany, 2009, ISBN: 978-3-642-13839-3.
Abstract | Links | BibTeX | Tags: cheminformatics, dimensionality reduction, inductive logic programming, relational learning, scientific knowledge, visualization
@conference{schulz2009,
title = {ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries},
author = {Hannes Schulz and Kristian Kersting and Andreas Karwath},
url = {http://dx.doi.org/10.1007/978-3-642-13840-9_20},
doi = {10.1007/978-3-642-13840-9_20},
isbn = {978-3-642-13839-3},
year = {2009},
date = {2009-01-01},
booktitle = {Inductive Logic Programming, 19th International Conference, ILP 2009},
pages = {209-216},
publisher = {Springer Verlag},
address = {Berlin Heidelberg, Germany},
organization = {Springer-Verlag Berlin Heidelberg},
crossref = {DBLP:conf/ilp/2009},
abstract = {Relational data is complex. This complexity makes one of the basic steps of ILP difficult: understanding the data and results. If the user cannot easily understand it, he draws incomplete conclusions. The situation is very much as in the parable of the blind men and the elephant that appears in many cultures. In this tale the blind work independently and with quite different pieces of information, thereby drawing very different conclusions about the nature of the beast. In contrast, visual representations make it easy to shift from one perspective to another while exploring and analyzing data. This paper describes a method for embedding interpretations and queries into a single, common Euclidean space based on their co-proven statistics. We demonstrate our method on real-world datasets showing that ILP results can indeed be captured at a glance.},
keywords = {cheminformatics, dimensionality reduction, inductive logic programming, relational learning, scientific knowledge, visualization},
pubstate = {published},
tppubtype = {conference}
}