MLCB 2020

We are excited to be holding the 15th Machine Learning in Computational Biology (MLCB) meeting, co-located with NeurIPS in Vancouver.  In its 2020 reincarnation, MLCB will be a two day virtual conference November 23 and 24, 9am-5pm PST. 

History: since its inception in 2004, and until 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).  Our sponsors include Recursion, Deep Genomics, and Amazon.

Announcement: Abstract submission deadline has been extended to Oct 2, 2020 at midnight PST.

MLCB co-founders/co-chairs 

Annual MLCB Meeting 2020

Format: MLCB is a two day conference. We will have four keynote presentations, one panel discussion, plenty of oral presentation based on abstract submissions, as we as a poster session. We are also organizing a couple of informal meet-ups around specific themes (e.g., a 'career panel')

• Time: November 23-24; see Schedule for the full program. 

• Location: Virtual link (coming soon)

• Poster and social hour link (list of posters here)

• Cost: Free!

• Please  Register here. 

• Important dates and submission information 

• Invited speakers' bios and abstracts

• Panel discussion participants and topics: see Schedule.

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.