Call For Papers, Abstracts and Demonstrations

Pacific Symposium on Biocomputing

Big Island of Hawaii - January 3-7, 2022

The PSB 2022 proceedings are available here.

The twenty-seventh Pacific Symposium on Biocomputing (PSB), will be held January 3-7, 2022 at the Fairmont Orchid on the Big Island of Hawaii. PSB will bring together top researchers from North America, the Asian Pacific nations, Europe and around the world to exchange research results and address open issues in all aspects of computational biology. PSB will provide a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. PSB intends to attract a balanced combination of computer scientists and biologists, presenting significant original research, demonstrating computer systems, and facilitating formal and informal discussions on topics of importance to computational biology.

To provide focus for the very broad area of biological computing, PSB is organized into a series of specific sessions. Each session will involve both formal research presentations and open discussion groups.

Papers and posters

The core of the conference consists of rigorously peer-reviewed full-length papers reporting on original work. All accepted papers will be published electronically and indexed in PubMed, and the best of these will be presented orally to the entire conference.

PSB's publisher, World Scientific Publishing (WSP), will initiate submission to PubMed Central (PMC) for accepted papers that must comply with the NIH Public Access Policy. Authors are responsible for ensuring that the manuscript is deposited into the NIHMS upon acceptance for publication. The author must complete all remaining steps in the NIHMS in order for the submission to be accepted.

Per WSP, authors may post their submitted manuscript (preprint) at any time on their personal website, in their company or institutional repository, in not-for-profit subject-based preprint servers or repositories, and on scholarly collaboration networks (SCNs) which have signed up to the STM sharing principles. Please provide the following applicable acknowledgement along with a link to the article via its DOI if available.

  • Preprint of an article submitted for consideration in Pacific Symposium on Biocomputing © [Year] World Scientific Publishing Co., Singapore, http://psb.stanford.edu/
  • Preprint of an article published in Pacific Symposium on Biocomputing © [Year] World Scientific Publishing Co., Singapore, http://psb.stanford.edu/ (for accepted PSB papers only).

Authors are encouraged to submit preprints to bioRxiv, an online archive and distribution service operated by Cold Spring Harbor Laboratory for preprints in the life sciences.

Researchers wishing to present their research without official publication are encouraged to submit a one page abstract by the abstract deadline listed below to present their work in the poster sessions.

Important dates

Paper submissions due (absolute deadline): August 2, 2021 11:59PM PT
Notification of paper acceptance: September 13, 2021
Final paper deadline: October 1, 2021 11:59PM PT
Abstract deadline: December 6, 2021 11:59PM PT
Meeting: January 3-7, 2022

Paper format

Please reference the PSB paper format template and instructions.
The accepted file format is PDF (Adobe Acrobat preferred). Attached files should be named with the last name of the first author (e.g. altman.pdf). Hardcopy submissions or unprocessed TEX or LATEX files or electronic submissions not submitted through the paper management system will be rejected without review.

Each paper must be accompanied by a cover letter. The cover letter should be the first page of your paper submission. The cover letter must state the following:

  • The email address of the corresponding author.
  • The specific PSB session that should review the paper.
  • The submitted paper contains original, unpublished results, and is not currently under consideration elsewhere.
  • All co-authors concur with the contents of the paper.

Submitted papers are limited to twelve (12) pages (not including the cover letter) in our publication format. The bibliography is included in the 12 page limit. Please format your paper according to instructions found at http://psb.stanford.edu/psb-online/psb-submit/. If figures cannot be easily resized and placed precisely in the text, then it should be clear that with appropriate modifications, the total manuscript length would be within the page limit. Color images are accepted for publication at no additional charge. Supplemental material may be referenced by URL (PSB will not host supplemental material).

Papers must be submitted to the PSB 2022 paper management system.

Contact PSB (psb.hawaii @ gmail.com) for additional information about paper submission requirements.

Travel support

We have been able to offer partial travel support to many PSB attendees in the past. However, please note that no one is guaranteed travel support. The online travel support application form will open in August.

PSB 2022 Sessions:

Each session has a chair who is responsible for organizing submissions. Please contact the specific session chair relevant to your interests for further information. Links on each of the session titles below lead to more detailed calls for participation for each session.

AI-driven Advances in Modeling of Protein Structure

Session Chairs: Krzysztof Fidelis, Sergei Grudinin

Results from the most recent CASP (Critical Assessment of Structure Prediction) experiment show dramatic improvement in computing the three-dimensional structure of proteins from amino acid sequence, with many models rivaling experimental structures in accuracy. These results suggest that deep learning approaches will also be effective for a range of related structural biology applications, including macromolecular assemblies, ligand docking, alternative conformations, disordered states, interpretation of genetic variants, and protein design. The session will bring together researchers from the computational structural biology and machine learning communities to explore this new landscape. We invite contributions addressing relevant questions of methodology, applications, and synergies with experimental structural biology.

  • Contact: Krzysztof Fidelis
  • Email: kfidelis at ucdavis dot edu

Big Data Imaging Genomics: Reproducible Findings, Individual Predictions, and Clinical Decisions

Session Chairs: Peter Kochunov, Li Shen, John Darrell Van Horn, Paul M. Thompson

The proposed session will solicit presentations to describe challenges and solutions in translating Big Data Imaging Genomic research and findings toward personalized medicine and guiding individual clinical decisions. We will focus on Big Data analyses, pattern recognition, machine learning and AI, electronic health records, guiding diagnostic and treatment decisions and reports of state-of-the-art findings from large and diverse imaging, genomics and other biomedical datasets.

  • Contacts: Peter Kochunov at pkochunov at gmail dot com and Kathryn Hatch at khatch at som dot umaryland dot edu

Human Intrigue: Meta Analysis Approaches for Big Questions with Big Data

Session Chairs: Carly A. Bobak, Meghan E. Muse, Kristine A. Giffin, Derek A. Williamson, Dennis P. Wall, Casey S. Greene, Jason Moore

High dimensional biological and ‘omics datasets present unique challenges with traditional meta-analysis approaches and are in need of innovation in computational methodology. In this session we are seeking papers which employ meta-analysis methodologies in applications related to biology and health as well as papers posing new meta-analysis approaches.

  • Contact: Carly Bobak
  • Email: Carly.A.Bobak.GR at dartmouth dot edu

Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare

Session Chairs: Steven E. Brenner, Martha L. Bulyk, Jonathan Chen, Dana C. Crawford, Roxana Daneshjou, Samuel G. Finlayson, Łukasz Kidziński

Our session will focus on machine learning and other novel methods that leverage either genomics or other forms of multi-modal clinical data in order to improve healthcare. These improvements may range from identifying ways to risk stratify or treat patients for higher value care to creating tools for mitigating bias in health systems.

  • Contact: Roxana Daneshjou and Marth Bulyk
  • Email: roxanad at stanford dot edu and mlbulyk at genetics dot med dot harvard dot edu