• Bioinformatics and machine learning to support nanomaterial grouping 

      Bahl, Aileen; Halappanavar, Sabina; Wohlleben, Wendel; Nymark, Penny; Kohonen, Pekka; Wallin, Håkan; Vogel, Ulla; Haase, Andrea (Peer reviewed; Journal article, 2024)
      Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. ...
    • Nanomaterial grouping: Existing approaches and future recommendations 

      Giusti, Anna; Atluri, Rambabu; Tsekovska, Rositsa; Gajewicz, Agnieszka; Apostolova, Margarita; Battistelli, Chiara L.; Bleeker, Eric; Bossa, Cecilia; Bouillard, Jaques; Dusinska, Maria; Gómez-Fernández, Paloma; Grafström, Roland; Gromelski, Maciej; Handzhiyski, Yordan; Jacobsen, Nicklas Raun; Jantunen, Paula; Jensen, Keld Alstrup; Mech, Agnieszka; Navas, José Maria; Nymark, Penny; Oomen, Agnes G.; Puzyn, Tomasz; Rasmussen, Kirsten; Riebeling, Christian; Rodriguez-LLopis, Isabel; Sabella, Stefania; Sintes, Juan Riego; Suarez-Merino, Blanca; Tanasescu, Speranta; Wallin, Håkan; Haase, Andrea (Peer reviewed; Journal article, 2019)
      The physico-chemical properties of manufactured nanomaterials (NMs) can be fine-tuned to obtain different functionalities addressing the needs of specific industrial applications. The physico-chemical properties of NMs ...