2017
Karwath, Andreas; Hubrich, Markus; Kramer, Stefan
Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings, Springer Springer International Publishing, Cham, 2017, ISBN: 978-3-319-59758-4.
Abstract | Links | BibTeX | Tags: alzheimer, artificial intelligence, deep learning, health data science, machine learning, medicine, visualization
@conference{karwath2017a,
title = {Convolutional Neural Networks for the Identification of Regions of Interests in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer's Disease},
author = {Andreas Karwath and Markus Hubrich and Stefan Kramer},
editor = {en Teije, Annette and Popow, Christian and Holmes, John H. and Sacchi, Lucia},
url = {http://dx.doi.org/10.1007/978-3-319-59758-4_36},
doi = {10.1007/978-3-319-59758-4_36},
isbn = {978-3-319-59758-4},
year = {2017},
date = {2017-06-21},
urldate = {2017-06-21},
booktitle = {Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings},
pages = {316-321},
publisher = {Springer International Publishing},
address = {Cham},
organization = {Springer},
abstract = {When diagnosing patients suffering from dementia based on imaging data like PET scans, the identification of suitable predictive regions of interest (ROIs) is of great importance. We present a case study of 3-D Convolutional Neural Networks (CNNs) for the detection of ROIs in this context, just using voxel data, without any knowledge given a priori. Our results on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the predictive performance of the method is on par with that of state-of-the-art methods, with the additional benefit of potential insights into affected brain regions.},
keywords = {alzheimer, artificial intelligence, deep learning, health data science, machine learning, medicine, visualization},
pubstate = {published},
tppubtype = {conference}
}
2014
Gütlein, Martin; Karwath, Andreas; Kramer, Stefan
CheS-Mapper 2.0 for visual validation of (Q)SAR models Journal Article
In: J. Cheminformatics, vol. 6, no. 1, pp. 41, 2014.
Abstract | Links | BibTeX | Tags: cheminformatics, data mining, graph mining, validation, visualization
@article{gutlein2014,
title = {CheS-Mapper 2.0 for visual validation of (Q)SAR models},
author = {Martin Gütlein and Andreas Karwath and Stefan Kramer},
url = {http://dx.doi.org/10.1186/s13321-014-0041-7},
doi = {10.1186/s13321-014-0041-7},
year = {2014},
date = {2014-09-23},
journal = {J. Cheminformatics},
volume = {6},
number = {1},
pages = {41},
abstract = {Background
Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking.
Results
We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints.
Conclusions
Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.
Graphical abstract
Comparing actual and predicted activity values with CheS-Mapper.},
keywords = {cheminformatics, data mining, graph mining, validation, visualization},
pubstate = {published},
tppubtype = {article}
}
Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking.
Results
We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints.
Conclusions
Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.
Graphical abstract
Comparing actual and predicted activity values with CheS-Mapper.
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}
}