MLCB 2023
Machine Learning in Computational Biology
The 18th Machine Learning in Computational Biology (MLCB2023) meeting will be a hybrid two day conference November 30 - December 1, with the in-person component held at the University of Washington, Seattle, WA, USA. We will have three keynote presentations, an academic and an industry panel, oral presentations based on paper submissions, and a poster session. The conference will be streamed live through YouTube and Zoom.
Please register here. Registration is free. We have limited in-person capacity, and in-person registration will be given to the first ~150 registrants that request it.
Updated 11/09: we have reached in-person capacity and closed the registration form. If you registered by mid October, and received an email to confirm your dates, then you are all set for in-person attendance. Unfortunately, we are not able to accommodate any more in-person attendance (unless you have gotten an Oral or Poster presentation).
Update 11/09: All talks will be broadcast live. A youtube stream link will be available here before the conference starts on Nov 30.
One minute poster videos are now on Youtube under the MLCB Channel.
Submissions can be 8-page papers (eligible for optional publication in the Proceedings of Machine Learning Research) or 2-page abstracts.
From its inception in 2004 to 2017, MLCB was an official NeurIPS workshop. Given the growth and maturity of the field, MLCB became an independent conference co-located with NeurIPS in 2019 (see MLCB2019). From 2020 (see MLCB2020, MLCB2021, MLCB2022), MLCB was held virtually due to COVID-19. The virtual conference format led to a record number of participants, which included 1000 registered participants via Zoom and > 3000 views on YouTube live stream. Our sponsors include Deep Genomics, and ShapeTX.
Organizing Committee
Selected proceedings for previous meetings
Scope of MLCB
The field of computational biology has seen dramatic growth over the past few years. A wide range of high-throughput omics and imaging technologies developed in the last decade now enable us to measure parts of a biological system at various resolutions—at the genome, epigenome, transcriptome, and proteome levels. These diverse technologies are now being used to study questions relevant to basic biology and human health. Fully realizing the scientific and clinical potential of these data requires developing novel supervised and unsupervised learning methods that are scalable, can accommodate heterogeneity, are robust to systematic noise and confounding factors, and provide mechanistic insights.
The goals of the MLCB meeting are to i) present emerging problems and innovative machine learning techniques in computational biology, and ii) generate discussion on how to best model the intricacies of biological data and synthesize and interpret results in light of the current work in the field.
In addition to talks by invited speakers, will also have the usual rigorous screening of contributed talks on novel learning approaches in computational biology. The targeted audience are people with interest in machine learning and applications to relevant problems from the life sciences. Many of the talks will be of interest to the broad machine learning community.
MLCB2022 Statistics
Number of registered participants: 1172
Number of Oral presentations: 16; Number of Spotlight presentations: 10; Number of posters: 68
Participant demographics:
