Comparing functional annotation analyses with Catmap. - Thomas Breslin; Patrik Edén; Morten Krogh (BMC Bioinform., 2004)

Ensembles of genetically trained artificial neural networks for survival analysis. - Jonas Kalderstam; Patrik Edén; Mattias Ohlsson (ESANN, 2013)

Training artificial neural networks directly on the concordance index for censored data using genetic algorithms. - Jonas Kalderstam; Patrik Edén; Pär-Ola Bendahl; Carina Strand; Mårten Fernö; Mattias Ohlsson (Artif. Intell. Medicine, 2013)

Transcriptional Regulation of Lineage Commitment - A Stochastic Model of Cell Fate Decisions. - Jose Teles; Cristina Pina; Patrik Edén; Mattias Ohlsson; Tariq Enver; Carsten Peterson (PLoS Comput. Biol., 2013)

Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems. - Najmeh Abiri; Björn Linse; Patrik Edén; Mattias Ohlsson (Neurocomputing, 2019)

A community effort to identify and correct mislabeled samples in proteogenomic studies. - Seungyeul Yoo; Zhiao Shi; Bo Wen; Soon Jye Kho; Renke Pan; Hanying Feng; Hong Chen; Anders Carlsson; Patrik Edén; Weiping Ma; Michael L. Raymer; Ezekiel J. Maier; et al. (Patterns, 2021)

Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer. - Daqu Zhang; Looket Dihge; Pär-Ola Bendahl; Ida Arvidsson; Magnus Dustler; Julia Ellbrant; Kim Gulis; Malin Hjärtström; Mattias Ohlsson; Cornelia Rejmer; David Schmidt; Sophia Zackrisson; et al. (npj Digit. Medicine, 2025)