Abstract is missing.
- CVQVAE: A representation learning based method for multi-omics single cell data integrationTianyu Liu 0005, Grant Greenberg, Ilan Shomorony. 1-15 [doi]
- Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVIEthan Weinberger, Romain Lopez, Jan-Christian Hütter, Aviv Regev. 16-32 [doi]
- Ensembling improves stability and power of feature selection for deep learning modelsPrashnna K. Gyawali, Xiaoxia Liu, James Zou, Zihuai He. 33-45 [doi]
- Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMsVidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence. 46-60 [doi]
- A generative recommender system with GMM prior for cancer drug generation and sensitivity predictionKrzysztof Koras, Marcin Mozejko, Paulina Szymczak, Adam Izdebski, Eike Staub, Ewa Szczurek. 61-73 [doi]
- Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy imagesAlexander Lin, Alex Lu. 74-93 [doi]
- Energy-based Modelling for Single-cell Data AnnotationTianyi Liu, Philip Fradkin, Lazar Atanackovic, Leo J. Lee. 94-109 [doi]
- Predicting Immune Escape with Pretrained Protein Language Model EmbeddingsKyle Swanson, Howard Chang, James Zou. 110-130 [doi]
- Selecting deep neural networks that yield consistent attribution-based interpretations for genomicsAntonio Majdandzic, Chandana Rajesh, Ziqi Tang, Shushan Toneyan, Ethan L. Labelson, Rohit K. Tripathy 0001, Peter K. Koo. 131-149 [doi]
- Language-Informed Basecalling Architecture for Nanopore Direct RNA SequencingAlexandra Sneddon, Pablo Acera Mateos, Nikolay Shirokikh, Eduardo Eyras. 150-165 [doi]
- Forecasting labels under distribution-shift for machine-guided sequence designLauren Berk Wheelock, Stephen Malina, Jeffrey Gerold, Sam Sinai. 166-180 [doi]