MLCB 2021

We are excited to be holding the 16th Machine Learning in Computational Biology (MLCB) meeting, co-located with NeurIPS in Vancouver. In its 2021 reincarnation, MLCB will be a two day virtual conference November 22 and 23, 9am-5pm PST.

History: 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). In 2020 (see MLCB2020), 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.

Announcement: Abstract submission deadline is Oct 1, 2021 at midnight PST. Deadline extended: abstracts are due Wed Oct 6, 2021 at midnight (your choice of time zone).

MLCB co-founders/co-chairs

Annual MLCB Meeting 2021

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. We are also organizing a couple of informal meet-ups around specific themes (e.g., a 'career panel')

• Time: November 22-23; schedule for the full program will be available in the fall.

• 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.