MLCB 2026
November 16-17 @ New York Genome Center, New York City
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Machine Learning in Computational Biology
The 21st Machine Learning in Computational Biology (MLCB) meeting will be a two-day hybrid conference, November 16-17, 9am-5pm ET, with the in-person component at the New York Genome Center, NYC. Registration will open when paper decisions are made. We have limited capacity, so please only register if you plan to attend in-person (and update your response through the form if you can no longer attend).
All talks will be streamed online on our Youtube channel.
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From its inception in 2004 to 2017, MLCB was a 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 and MLCB2024 were hybrid with the in person component in Seattle, WA. MLCB2025 was held in NYC.
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 those with an 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.
Acknowledgements
Acknowledgements
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
Thank you to eyes Robson for helping us refresh our review questions for 2025!
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