Auflistung nach Schlagwort "Machine learning"

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2021-11-02Zeitschriftenartikel

Machine Learning for Health: Algorithm Auditing & Quality Control

Oala, Luis; Murchison, Andrew G.; Balachandran, Pradeep; Choudhary, Shruti; Fehr, Jana; Leite, Alixandro Werneck; Goldschmidt, Peter G.; Johner, Christian; Schörverth, Elora D. M.; Nakasi, Rose; Meyer, Martin; Cabitza, Federico; Baird, Pat; Prabhu, Carolin; Weicken, Eva; Liu, Xiaoxuan; Wenzel, Markus; Vogler, Steffen; Akogo, Darlington; Alsalamah, Shada; Kazim, Emre; Koshiyama, Adriano; Piechottka, Sven; Macpherson, Sheena; Shadforth, Ian; Geierhofer, Regina; Matek, Christian; Krois, Joachim; Sanguinetti, Bruno; Arentz, Matthew; Bielik, Pavol; Calderon‑Ramirez, Saul; Abbood, Auss; Langer, Nicolas; Haufe, Stefan; Kherif, Ferath; Pujari, Sameer; Samek, Wojciech; Wiegand, Thomas

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the ...
2019-07-09Zeitschriftenartikel

Recursive feature elimination in random forest classification supports nanomaterial grouping

Bahl, Aileen; Hellack, Bryan; Balas, Mihaela; Dinischiotu, Anca; Wiemann, Martin; Brinkmann, Joep; Luch, Andreas; Renard, Bernhard Y.; Haase, Andrea

Nanomaterials (NMs) can be produced in numerous different variants of the same chemical substance. An in-depth safety assessment for each variant by generating test data will simply not be feasible. Thus, NM grouping ...
2015-07-30Zeitschriftenartikel

SuRankCo: supervised ranking of contigs in de novo assemblies

Kuhring, Mathias; Dabrowski, Piotr Wojtek; Piro, Vitor C.; Nitsche, Andreas; Renard, Bernhard Y.

Background: Evaluating the quality and reliability of a de novo assembly and of single contigs in particular is challenging since commonly a ground truth is not readily available and numerous factors may influence ...