MLCB 2023

We are excited to be holding the 18th Machine Learning in Computational Biology (MLCB) meeting.  In its 2023 incarnation, MLCB will be a two day hybrid conference November 30 and Dec 1, 9am-5pm PST. The in-person component will be held at the campus of University of Washington, Seattle WA. The event will be streamed live through youtube and Zoom. 

From its inception in 2004 to 2017, MLCB was an official NeurIPS workshop (previous meetings 2004-2017). 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 pandemic). 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 Recursion, Deep Genomics, and Amazon.

Abstract submission deadline Oct 4, 2023 at midnight PST. 

2023 MLCB co-organizers 

Annual MLCB Meeting 2023

Format: MLCB is a two day conference. We will have three keynote presentations, one panel discussion, plenty of oral presentations based on abstract submissions, as well as a poster session and an industry panel. 

• Time: November 30-Dec 1; schedule for the full program will be available early November. 

• Poster and social hour link: TBD

• Cost: Free!

• Please register here (will be open in June)

• Important dates and submission information 

• Invited speakers' bios and abstracts

• Panel discussion participants and topics.

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, including NIPS participants without any existing research link to computational biology. Many of the talks will be of interest to the broad machine learning community.