%0 Journal Article %1 Blee2022-bs %A Blee, Alexandra M %A Li, Bian %A Pecen, Turner %A Meiler, Jens %A Nagel, Zachary D %A Capra, John A %A Chazin, Walter J %D 2022 %J Cancer Res. %K imported %N 15 %P 2704--2715 %T An active learning framework improves tumor variant interpretation %V 82 %X SIGNIFICANCE: A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.

@article{Blee2022-bs, abstract = {SIGNIFICANCE: A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.}, added-at = {2024-10-02T10:38:17.000+0200}, author = {Blee, Alexandra M and Li, Bian and Pecen, Turner and Meiler, Jens and Nagel, Zachary D and Capra, John A and Chazin, Walter J}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2e4c8afdeece06eba3a1e0c45ed676037/scadsfct}, interhash = {62f887c733caba5201245fa10c88d63a}, intrahash = {e4c8afdeece06eba3a1e0c45ed676037}, journal = {Cancer Res.}, keywords = {imported}, language = {en}, month = aug, number = 15, pages = {2704--2715}, timestamp = {2024-10-02T10:38:17.000+0200}, title = {An active learning framework improves tumor variant interpretation}, volume = 82, year = 2022 }