Machine Learning in Computational Biology
The full MLCB recordings are available on Youtube:
Day 1 link: http://www.youtube.com/watch?v=BYanbnKpwok
Day 2 link: https://www.youtube.com/watch?v=8co5_aqBwGQ
Important information regarding joining the virtual meeting:
+ Because of zoom webinar capacity limit, we have sent direct zoom link to the first 1000 registered participants based on the time of registration.
+ If you didn't receive a direct email with zoom link: We will post a video stream link (via youtube) right before the conference, which you can use to view talks/content.
+ Poster presentation and social hour will be done through gather.town link here.
+ If you are presenting an oral or spotlight, you should have received a zoom link to log on as a Panelist.
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.
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)
Please Register here.
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.
+ Number of registered participants: 1131 (as of Nov 21/2022)
+ Number of submitted papers: 91; Number of Oral presentations: 14; Number of Spotlight presentations: 9; Number of posters: 39
+ Participant demographics: