%dl
  %dt
    [
    %a{ :name => "maunz08b" }
      1
    ]
  %dd
    A. Maunz and C. Helma.
    Prediction of toxic effects of pharmaceutical agents.
    In K. V. Balakin, editor,
    %em
      Pharmaceutical Data Mining: Approaches
      and Applications for Drug Discovery
    , New York, 2009. J. Wiley & Sons, Inc.
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    [
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      2
    ]
  %dd
    A. Maunz, C. Helma, and S. Kramer.
    Large-scale graph mining using backbone refinement classes.
    In
    %em
      Proceedings of the 15th ACM SIGKDD International Conference
      on Knowledge Discovery and Data Mining (KDD-09)
    , page in press, 2009.
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    [
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      3
    ]
  %dd
    F. Hammann, H. Gutmann, U. Jecklin, A. Maunz, C. Helma, and J. Drewe.
    Development of decision tree models for substrates, inhibitors and
    inducers of P-glycoprotein.
    %em
      Current Drug Metabolism
    , 10:339-46, 2009.
  %dt
    [
    %a{ :name => "hammann09b" }
      4
    ]
  %dd
    Felix Hammann, Heike Gutmann, Ulli Baumann, Christoph Helma, and Juergen Drewe.
    Classification of cytochrome p(450) activities using machine learning
    methods.
    %em
      Molecular pharmaceutics
    , 6:1920-6, 2009.
  %dt
    [
    %a{ :name => "maunz08a" }
      5
    ]
  %dd
    A. Maunz and C. Helma.
    Prediction of chemical toxicity with local support vector regression
    and activity-specific kernels.
    %em
      SAR QSAR Environ. Res.
    , 19:413-431, 2008.
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      \.pdf
    \ ]
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    [
    %a{ :name => "benigni07" }
      6
    ]
  %dd
    R. Benigni, T. I. Netzeva, E. Benfenati, C. Bossa, R. Franke, C. Helma,
    E. Hulzebos, C. Marchant, A. Richard, Y.-T. Woo, and C. Yang.
    The expanding role of predictive toxicology: an update on the (Q)SAR
    models for mutagens and carcinogens.
    %em
      J Environ Sci Health C Environ Carcinog Ecotoxicol Rev.
    ,
    25:53-97, 2007.
  %dt
    [
    %a{ :name => "helma06a" }
      7
    ]
  %dd
    C. Helma.
    Lazy structure-activity relationships (
    %tt
      lazar
    ) for the
    prediction of rodent carcinogenicity and
    %em
      Salmonella
    mutagenicity.
    %em
      Molecular Diversity
    , 10:147-158, 2006.
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      \.pdf
    \ ]
  %dt
    [
    %a{ :name => "helma06b" }
      8
    ]
  %dd
    C. Helma and J. Kazius.
    Artificial intelligence and data mining for toxicity prediction.
    %em
      Current Computer-Aided Drug Design
    , 2:1-19, 2006.
    [ 
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      \.pdf
    \ ]
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    [
    %a{ :name => "vracko06" }
      9
    ]
  %dd
    M. Vracko, V. Bandelj, P. Barbieri, E. Benfenati, Q. Chaudhry, M. Cronin,
    J. Devillers, A. Gallegos, G. Gini, P. Gramatica, C. Helma, D. Neagu,
    T. Netzeva, M. Pavan, G. Patlevicz, M. Randic, I. Tsakovska, and A. Worth.
    Validation of counter propagation neural network models for
    predictive toxicology according to the OECD principles. A case study.
    %em
      SAR QSAR Environ. Res.
    , 17:265-84, 2006.
  %dt
    [
    %a{ :name => "helma05a" }
      10
    ]
  %dd
    C. Helma.
    %em
      Predictive Toxicology
    \.
    CRC Press, Boca Raton, 2005.
  %dt
    [
    %a{ :name => "helma05b" }
      11
    ]
  %dd
    C. Helma.
    %tt
      lazar:
    Lazy Structure - Activity Relationships for toxicity
    prediction.
    In C. Helma, editor,
    %em
      Predictive Toxicology
    , pages 479-499. CRC
    Press, Boca Raton, 2005.
  %dt
    [
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      12
    ]
  %dd
    C. Helma.
    %em
      In Silico
    predictive toxicology: The state-of-the-art and
    strategies to predict human health effects.
    %em
      Current Opinions in Drug Discovery & Development
    , 8:27-31,
    2005.
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    \ ]
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    [
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      13
    ]
  %dd
    C. Helma.
    A brief introduction to predictive toxicology.
    In C. Helma, editor,
    %em
      Predictive Toxicology
    \. CRC Press, Boca
    Raton, 2005.
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    [
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      14
    ]
  %dd
    S. Kramer and C. Helma.
    Machine learning and data mining.
    In C. Helma, editor,
    %em
      Predictive Toxicology
    , pages 223-254. CRC
    Press, Boca Raton, 2005.
  %dt
    [
    %a{ :name => "helma04a" }
      15
    ]
  %dd
    C. Helma, T. Kramer, S. Kramer, and L. DeRaedt.
    Data Mining and Machine Learning techniques for the
    identification of mutagenicity inducing substructures and
    Structure-Activity Relationships of noncongeneric compounds.
    %em
      J. Chem. Inf. Comput. Sci.
    , 44:1402-1411, 2004.
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      http
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      16
    ]
  %dd
    C. Helma.
    Data mining and knowledge discovery in predictive toxicology.
    %em
      SAR QSAR Environ. Res.
    , 15:367-383, 2004.
  %dt
    [
    %a{ :name => "helma03a" }
      17
    ]
  %dd
    C. Helma and S. Kramer.
    A survey of the Predictive Toxicology Challenge.
    %em
      Bioinformatics
    , 19:1179-1182, 2003.
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      http
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    [
    %a{ :name => "helma03b" }
      18
    ]
  %dd
    C. Helma, S. Kramer, and L. DeRaedt.
    The molecular feature miner MolFea.
    In M. Hicks and C. Kettner, editors,
    %em
      Proceedings of the
      Beilstein Workshop 2002: Molecular Informatics: Confronting Complexity
    ,
    pages 79-93. Beilstein Institut, Frankfurt, 2003.
  %dt
    [
    %a{ :name => "toivonen03" }
      19
    ]
  %dd
    H. Toivonen, A. Srinivasan, R. D. King, S. Kramer, and C. Helma.
    Statistical evaluation of the Predictive Toxicology Challenge
    2000-2001.
    %em
      Bioinformatics
    , 19:1183-1193, 2003.
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      http
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    [
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      20
    ]
  %dd
    C. Helma.
    Data mining in toxicology.
    In J.P.V. Heuvel, W.F. Greenlee, G.H. Perdew, and W.B. Mattes,
    editors,
    %em
      Comprehensive Toxicology Vol XX. Cellular and Molecular
      Toxicology
    , pages 611-624. Elsevier, 2002.
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    [
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      21
    ]
  %dd
    F. Kassie, S. Rabot, M. Uhl, W. Huber, H.M. Qin, C. Helma, R. Schulte-Hermann,
    and S. Knasmüller.
    Chemoprotective effects of garden cress (lepidium sativum) and its
    constituents towards 2-amino-3-methyl-imidazo[4,5-f]quinoline (IQ)-induced
    genotoxic effects and colonicneoplastic lesions.
    %em
      Carcinogenesis
    , 23:1155-1161, 2002.
  %dt
    [
    %a{ :name => "kramer02" }
      22
    ]
  %dd
    S. Kramer, E. Frank, and C. Helma.
    Fragment generation and Support Vector Machines for inducing
    SARs.
    %em
      SAR and QSAR in Environmental Research
    , 13:509-523, 2002.
  %dt
    [
    %a{ :name => "gottmann01" }
      23
    ]
  %dd
    E. Gottmann, S. Kramer, B. Pfahringer, and C. Helma.
    Data quality in predictive toxicology: Reproducibility of rodent
    carcinogenicity experiments.
    %em
      Environ. Health Perspect.
    , 109:509-514, 2001.
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      \.html
    \ ]
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    [
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      24
    ]
  %dd
    C. Helma, R.D. King, S. Kramer, and A. Srinivasan.
    The Predictive Toxicology Challenge 2000-2001.
    %em
      Bioinformatics
    , 17:107-108, 2001.
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      \.pdf
    \ ]
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    [
    %a{ :name => "kramer01e" }
      25
    ]
  %dd
    S. Kramer, L. De Raedt, and C. Helma.
    Molecular feature mining in HIV data.
    In
    %em
      Proceedings of the Seventh ACM SIGKDD International
      Conference on Knowledge Discovery and Data Mining (KDD-01)
    , pages 136-143,
    2001.
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      26
    ]
  %dd
    C. Helma, S. Kramer, B. Pfahringer, and E. Gottmann.
    Data quality in predictive toxicology: Identification of chemical
    structures and calculation of chemical properties.
    %em
      Environ. Health Perspect.
    , 108:1029-1033, 2000.
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      27
    ]
  %dd
    C. Helma, E. Gottmann, and S. Kramer.
    Knowledge discovery and data mining in toxicology.
    %em
      Stat Methods Med Res.
    , 9:329-358, 2000.
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      28
    ]
  %dd
    C. Helma, S. Kramer, and B. Pfahringer.
    Carcinogenicity prediction for noncongeneric compounds: Experiments
    with the Machine Learning Program SRT and various sets of chemical
    descriptors.
    In
    %em
      Molecular Modelling and Prediction of Bioactivity
    , page 464,
    2000.
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    ]
  %dd
    C. Helma and M. Uhl.
    A public domain image analysis program for the single cell gel
    electrophoresis (comet) assay.
    %em
      Mutation Res.
    , 466:9-15, 2000.
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      30
    ]
  %dd
    C. Helma, E. Gottmann, S. Kramer, and B. Pfahringer.
    The application of machine learning algorithms to detect chemical
    properties responsible for carcinogenicity.
    In K. Gundertofte and F.S. Jorgensen, editors,
    %em
      Molecular
      Modeling and Prediction of Bioactivity
    , pages 464-466, New York, 2000.
    Kluwer Academic/Plenum Publishers.
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      31
    ]
  %dd
    C. Helma, E. Gottmann, S. Kramer, and B. Pfahringer.
    Artificial Intelligence Methoden zur Vorhersage der
    Kanzerogenität organischer Verbindungen.
    In H. Schöffl, H. Spielmann, and H.A. Tritthart, editors,
    %em
      Ersatz- und Ergänzungsmethoden zu Tierversuchen: Forschung ohne
      Tierversuche
    , pages 128-134, Wien-New York, 2000. Springer Verlag.
  %dt
    [
    %a{ :name => "uhl00" }
      32
    ]
  %dd
    M. Uhl, C. Helma, and S. Knasmüller.
    Evaluation of the single cell gel electrophoretic assay with human
    hepatoma (HepG2) cells.
    %em
      Mutation Res.
    , 468:213-225, 2000.
  %dt
    [
    %a{ :name => "helma99a" }
      33
    ]
  %dd
    C. Helma, E. Gottmann, S. Kramer, and B. Pfahringer.
    Data quality issues in toxicological knowledge discovery.
    In
    %em
      Predictive Toxicology of Chemicals: Experiences and Impact of
      AI Tools
    , pages 8-11, Menlo Park, California, 1999. AAAI Press.
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    [
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    ]
  %dd
    B. Pfahringer, E. Gottmann, C. Helma, and S. Kramer.
    Efficiency/representational issues in toxicological knowledge
    discovery.
    In
    %em
      Predictive Toxicology of Chemicals: Experiences and Impact of
      AI Tools
    , pages 86-89, Menlo Park, California, 1999. AAAI Press.
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    [
    %a{ :name => "uhl99" }
      35
    ]
  %dd
    M. Uhl, C. Helma, and S. Knasmüller.
    Single-cell gel electrophoresis assays with human-derived hepatoma
    (HepG2) cells.
    %em
      Mutation Res.
    , 441:215-224, 1999.
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    [
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      36
    ]
  %dd
    C. Helma, E. Gottmann, S. Kramer, and B. Pfahringer.
    The application of machine learning algorithms to detect chemical
    properties responsible for carcinogenicity.
    In
    %em
      Molecular Modelling and Prediction of Bioactivity.
      Proceedings of the 12th European Symposium on Quantitative Structure-Activity
      Relationships
    , 1998.
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    ]
  %dd
    C. Helma, P. Eckl, E. Gottmann, F. Kassie, W. Rodinger, H. Steinkellner,
    C. Windpassinger, R. Schulte-Hermann, and S. Knasmüller.
    Genotoxic and ecotoxic effects of groundwaters and their relation to
    routinely measured chemical parameters.
    %em
      Environ. Sci. Technol.
    , 32:1799-1805, 1998.
  %dt
    [
    %a{ :name => "knasmueller98" }
      38
    ]
  %dd
    S. Knasmüller, C. Helma, P. Eckl, E. Gottmann, H. Steinkellner, F. Kassie,
    T. Haider, W. Parzefall, and R. Schulte-Hermann.
    Investigations on genotoxic effects of groundwater from the
    Mitterndorfer Senke from the vicinity of Wiener Neustadt.
    %em
      Wiener Klinische Wochenschrift
    , 110:824-833, 1998.
  %dt
    [
    %a{ :name => "kramer98" }
      39
    ]
  %dd
    S. Kramer, B. Pfahringer, and C. Helma.
    Stochastic propositionalization of non-determinate background
    knowledge.
    In
    %em
      Proceedings of the 8th International Workshop on Inductive
      Logic Programming (ILP-98)
    , pages 80-94, Berlin Heidelberg New York, 1998.
    Springer.
  %dt
    [
    %a{ :name => "steinkellner98" }
      40
    ]
  %dd
    H. Steinkellner, M.S. Kong, C. Helma, S. Ecker, T.H. Ma, O. Horak, M. Kundi,
    and S. Knasmüller.
    Genotoxic effects of heavy metals: Comparative investigation with
    plant bioassays.
    %em
      Environ. Mol. Mutagen.
    , 31:183-191, 1998.
  %dt
    [
    %a{ :name => "helma97a" }
      41
    ]
  %dd
    C. Helma and S. Knasmüller.
    Die Belastung von Gewässern mit gentoxischen Substanzen
    IIa: Die Quellen gentoxischer Kontaminationen (Industrielle
    Emissionen).
    %em
      UWSF-Z.Umweltchem.Ökotox.
    , 9:41-48, 1997.
  %dt
    [
    %a{ :name => "helma97b" }
      42
    ]
  %dd
    C. Helma and S. Knasmüller.
    Die Belastung von Gewässern mit gentoxischen Substanzen
    IIb: Die Quellen gentoxischer Kontaminationen (Nichtindustrielle
    Emissionen).
    %em
      UWSF-Z.Umweltchem.Ökotox.
    , 9:163-168, 1997.
  %dt
    [
    %a{ :name => "helma97c" }
      43
    ]
  %dd
    C. Helma.
    Die Situation der Chlorchemie in Östereich: Chlorhältige
    Pflanzenschutz- Holzschutz und Wasserdesinfektionsmittel - Entwicklungen im
    Zeitraum 1993 bis 1997.
    Technical report, Gesellschaft Österreichischer Chemiker, 1997.
  %dt
    [
    %a{ :name => "knasmueller97" }
      44
    ]
  %dd
    S. Knasmüller, W. Parzefall, C. Helma, F. Kassie, S. Ecker, and
    R. Schulte-Hermann.
    Toxic effects of griseofulvin: Disease models, mechanisms, and risk
    assessment.
    %em
      Critical Reviews in Toxicology
    , 27:495-537, 1997.
  %dt
    [
    %a{ :name => "kramer97a" }
      45
    ]
  %dd
    S. Kramer, B. Pfahringer, and C. Helma.
    Mining for causes of cancer: Machine learning experiments at various
    levels of detail.
    In
    %em
      Proceedings of the Third International Conference on
      Knowledge Discovery and Data Mining (KDD-97)
    , Menlo Park, CA, 1997. AAAI
    Press.
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    [
    %a{ :name => "helma96a" }
      46
    ]
  %dd
    C. Helma, V. Mersch-Sundermann, V.S. Houk, U. Glasbrenner, C. Klein,
    L. Wenquing, K. Fekadu, R. Schulte-Hermann, and S. Knasmüller.
    Comparative evaluation of four bacterial assays for the detection of
    genotoxic effects in water samples.
    %em
      Environ. Sci. Technol.
    , 30:897-907, 1996.
  %dt
    [
    %a{ :name => "helma95" }
      47
    ]
  %dd
    C. Helma, L. Kronberg, T.H. Ma, and S. Knasmüller.
    Genotoxic effects of the chlorinated hydroxyfuranones
    3-chloro-4-(dichloromethyl)-5-hydroxy-2[5H]-furanone and
    3,4-dichloro-5-hydroxy-2[5H]-furanone in
    %i
      Tradescantia
    micronucleus assays.
    %em
      Mutation Res.
    , 346:181-186, 1995.
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    [
    %a{ :name => "helma95b" }
      48
    ]
  %dd
    C. Helma.
    Erfahrungen mit dem Einsatz biologischer Mutationstests zur
    Verfahrensoptimierung und Umweltkontrolle.
    In
    %em
      Handbuch der Umwelttechnik 1995
    , pages 266-267. Wien, 1995.
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    [
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      49
    ]
  %dd
    C. Helma.
    Detection of genotoxic contaminations in water.
    In M. Guminska and J. Miedzobrodzki, editors,
    %em
      Molecular Biology
      for Environment and Man
    , pages 99-111. 1995.
  %dt
    [
    %a{ :name => "helma94a" }
      50
    ]
  %dd
    C. Helma, R. Sommer, R. Schulte-Hermann, and S. Knasmüller.
    Enhanced clastogenicity of contaminated ground water following UV
    irradiation detected by the
    %em
      Tradescantia
    micronucleus assay.
    %em
      Mutation Res.
    , 323:93-98, 1994.
  %dt
    [
    %a{ :name => "helma94b" }
      51
    ]
  %dd
    C. Helma, S. Knasmüller, and R. Schulte-Hermann.
    Die Belastung von Wässern mit gentoxischen Substanzen I. Methoden
    zur Prüfung der Gentoxizität.
    %em
      UWSF-Z.Umweltchem.Ökotox.
    , 6:277-288, 1994.
  %dt
    [
    %a{ :name => "helma94c" }
      52
    ]
  %dd
    C. Helma, S. Knasmüller, and R. Schulte-Hermann.
    Biologische Tests zur Prüfung der Gentoxizität:
    Einsatzmöglichkeiten zur Verfahrensoptimierung und Emissionskontrolle.
    In Brandl et al., editor,
    %em
      Ecoinforma 94. Bd 9, Kanzerogenese
      durch Umweltchemikalien
    , pages 497-512. Wien, 1994.
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    [
    %a{ :name => "fassold93" }
      53
    ]
  %dd
    E. Fassold, C. Helma, S. Knasmüller, and R. Sattelberger.
    Die Situation der Chlorchemie in Östereich, 8. Teil:
    Chlorhältige Pflanzenschutz- Holzschutz und Wasserdesinfektionsmittel.
    Technical report, Gesellschaft Österreichischer Chemiker, 1993.
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    [
    %a{ :name => "helma92" }
      54
    ]
  %dd
    C. Helma et.al.
    Abfallarme Großküche.
    Technical report, Österreichisches Ökologie Institut, 1992.
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    [
    %a{ :name => "helma91" }
      55
    ]
  %dd
    C. Helma et.al.
    Ausstieg aus der Müllverbrennung.
    Technical report, Österreichisches Ökologie Institut, 1991.