%0 Journal Article %1 LiuRos2024a %A Liu, Bin %A Rosenhahn, Bodo %A Illig, Thomas %A DeLuca, David S. %D 2024 %J PLOS Computational Biology %K autoencoder leibnizailab myown %N 7 %P 1-22 %R 10.1371/journal.pcbi.1011198 %T A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data %V 20

@article{LiuRos2024a, added-at = {2024-10-08T15:45:31.000+0200}, author = {Liu, Bin and Rosenhahn, Bodo and Illig, Thomas and DeLuca, David S.}, biburl = {https://www.bibsonomy.org/bibtex/258085c773b616a9a715f8a873d41bac0/tntl3s}, doi = {10.1371/journal.pcbi.1011198}, interhash = {277984a9684647601c304269df6d9ec5}, intrahash = {58085c773b616a9a715f8a873d41bac0}, journal = {PLOS Computational Biology}, keywords = {autoencoder leibnizailab myown}, month = jul, number = 7, pages = {1-22}, timestamp = {2024-10-08T15:45:31.000+0200}, title = {A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data}, volume = 20, year = 2024 }