MLCB 2024
September 5 & 6 @ University of Washington, Seattle WA
Embedded Files
Machine Learning in Computational Biology
The MLCB 2024 proceedings are available through PMLR: https://proceedings.mlr.press/v261/
Recordings of the talks are available on our YouTube channel.
Registration is free. We have limited in-person capacity, and in-person registration will be given to the first ~150 registrants that request it. Please register here. Registration is closed. Note: all talks will be streamed online (see links above).
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 2020-2022 (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 the YouTube live stream. MLCB2023 was hybrid with the in person component in Seattle, WA. Our sponsors include Deep Genomics, and ShapeTX.
Organizing Committee
Organizing Committee
Selected proceedings for previous meetings
Selected proceedings for previous meetings
Scope of MLCB
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
MLCB2022 Statistics
Number of registered participants: 1172
Number of Oral presentations: 16; Number of Spotlight presentations: 10; Number of posters: 68
Participant demographics:
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