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   0 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=33052, pid=124)
      originalId => protected33052 (integer)
      authors => protected'Gasser, L.; Schür, C.; Perez-Cruz, F.; Schirmer, K.; Ba
         ity-Jesi, M.' (93 chars)
      title => protected'Machine learning-based prediction of fish acute mortality: implementation, i
         nterpretation, and regulatory relevance' (115 chars)
      journal => protected'Environmental Science: Advances' (31 chars)
      year => protected2024 (integer)
      volume => protected3 (integer)
      issue => protected'8' (1 chars)
      startpage => protected'1124' (4 chars)
      otherpage => protected'1138' (4 chars)
      categories => protected'' (0 chars)
      description => protected'Regulation of chemicals requires knowledge of their toxicological effects on
          a large number of species, which has traditionally been acquired through in
          vivo testing. The recent effort to find alternatives based on machine learn
         ing, however, has not focused on guaranteeing transparency, comparability an
         d reproducibility, which makes it difficult to assess advantages and disadva
         ntages of these methods. Also, comparable baseline performances are needed. 
         In this study, we trained regression models on the ADORE "t-F2F" challenge p
         roposed in [Schür et al., Nature Scientific data, 2023] to predict acute mo
         rtality, measured as LC50 (lethal concentration 50), of organic compounds on
          fishes. We trained LASSO, random forest (RF), XGBoost, Gaussian process (GP
         ) regression models, and found a series of aspects that are stable across mo
         dels: (i) using mass or molar concentrations does not affect performances; (
         ii) the performances are only weakly dependent on the molecular representati
         ons of the chemicals, but (iii) strongly on how the data is split. Overall, 
         the tree-based models RF and XGBoost performed best and we were able to pred
         ict the log10-transformed LC50 with a root mean square error of 0.90, which 
         corresponds to an order of magnitude on the original LC50 scale. On a local 
         level, on the other hand, the models are not able to consistently predict th
         e toxicity of individual chemicals accurately enough. Predictions for single
          chemicals are mostly influenced by a few chemical properties while taxonomi
         c traits are not captured sufficiently by the models. We discuss technical a
         nd conceptual improvements for these challenges to enhance the suitability o
         f in silico methods to environmental hazard assessment. Accordingly, this wo
         rk showcases state-of-the-art models and contributes to the ongoing discussi
         on on regulatory integration.' (1853 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'10.1039/d4va00072b' (18 chars)
      uid => protected33052 (integer)
      _localizedUid => protected33052 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected33052 (integer)modified
      pid => protected124 (integer)
   1 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=32142, pid=124)
      originalId => protected32142 (integer)
      authors => protected'Schür, C.; Gasser, L.; Perez-Cruz, F.; Schirmer, K.; Ba
         ity-Jesi, M.' (93 chars)
      title => protected'A benchmark dataset for machine learning in ecotoxicology' (57 chars)
      journal => protected'Scientific Data' (15 chars)
      year => protected2023 (integer)
      volume => protected10 (integer)
      issue => protected'1' (1 chars)
      startpage => protected'718 (20 pp.)' (12 chars)
      otherpage => protected'' (0 chars)
      categories => protected'' (0 chars)
      description => protected'The use of machine learning for predicting ecotoxicological outcomes is prom
         ising, but underutilized. The curation of data with informative features req
         uires both expertise in machine learning as well as a strong biological and 
         ecotoxicological background, which we consider a barrier of entry for this k
         ind of research. Additionally, model performances can only be compared acros
         s studies when the same dataset, cleaning, and splittings were used. Therefo
         re, we provide <em>ADORE</em>, an extensive and well-described dataset on ac
         ute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, 
         and algae). The core dataset describes ecotoxicological experiments and is e
         xpanded with phylogenetic and species-specific data on the species as well a
         s chemical properties and molecular representations. Apart from challenging 
         other researchers to try and achieve the best model performances across the 
         whole dataset, we propose specific relevant challenges on subsets of the dat
         a and include datasets and splittings corresponding to each of these challen
         ge as well as in-depth characterization and discussion of train-test splitti
         ng approaches.' (1154 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'10.1038/s41597-023-02612-2' (26 chars)
      uid => protected32142 (integer)
      _localizedUid => protected32142 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected32142 (integer)modified
      pid => protected124 (integer)