


default search action
MLCB 2022, Online
- David A. Knowles, Sara Mostafavi, Su-In Lee:

Machine Learning in Computational Biology, 21-22 November 2022, Online. Proceedings of Machine Learning Research 200, PMLR 2022 - Tianyu Liu, Grant Greenberg, Ilan Shomorony:

CVQVAE: A representation learning based method for multi-omics single cell data integration. 1-15 - Ethan Weinberger, Romain Lopez, Jan-Christian Hütter, Aviv Regev:

Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI. 16-32 - Prashnna K. Gyawali

, Xiaoxia Liu, James Zou, Zihuai He:
Ensembling improves stability and power of feature selection for deep learning models. 33-45 - Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence:

Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. 46-60 - Krzysztof Koras, Marcin Mozejko, Paulina Szymczak, Adam Izdebski, Eike Staub, Ewa Szczurek:

A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction. 61-73 - Alexander Lin, Alex Lu:

Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images. 74-93 - Tianyi Liu, Philip Fradkin, Lazar Atanackovic, Leo J. Lee:

Energy-based Modelling for Single-cell Data Annotation. 94-109 - Kyle Swanson, Howard Chang, James Zou:

Predicting Immune Escape with Pretrained Protein Language Model Embeddings. 110-130 - Antonio Majdandzic, Chandana Rajesh, Ziqi Tang, Shushan Toneyan, Ethan L. Labelson, Rohit K. Tripathy, Peter K. Koo:

Selecting deep neural networks that yield consistent attribution-based interpretations for genomics. 131-149 - Alexandra Sneddon

, Pablo Acera Mateos, Nikolay Shirokikh, Eduardo Eyras:
Language-Informed Basecalling Architecture for Nanopore Direct RNA Sequencing. 150-165 - Lauren Berk Wheelock, Stephen Malina, Jeffrey Gerold, Sam Sinai:

Forecasting labels under distribution-shift for machine-guided sequence design. 166-180

manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.


Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID














