Call For Papers, Abstracts and Demonstrations

Pacific Symposium on Biocomputing

Virtual Big Island of Hawaii January 5-7, 2021

PSB will take place next January 5-7, 2021. We are accepting, reviewing and publishing papers in the annual PSB proceedings. As always, proceedings papers are indexed in PubMed and are available open access.

Due to COVID-19, PSB 2021 will be virtual. The conference will be held during the scheduled dates of January 5-7, 2021 with exact times, schedule, and format to be determined in early September. We look forward to returning to our home on the Big Island in January 2022.

The PSB 2021 proceedings are available here.

The twenty-six Pacific Symposium on Biocomputing (PSB), will be held January 3-7, 2021 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,
  • Preprint of an article published in Pacific Symposium on Biocomputing © [Year] World Scientific Publishing Co., Singapore, (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 3, 2020 11:59PM PT
Notification of paper acceptance: September 14, 2020
Final paper deadline: October 1, 2020 11:59PM PT
Abstract deadline: December 15, 2020 11:59PM PT
Meeting: January 5-7, 2021

Paper format

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 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 2021 paper management system.

Contact PSB (psb.hawaii @ 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 2021 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.

Advanced Methods for Big Data Analytics in Women's Health

Session Chair: Graciela Gonzalez-Hernandez; Co-Chairs: Karin Verspoor, Maricel G Kann, Su Golder, Lisa Levine, Mary Regina Boland, Natalia Villanueva-Rosales, Karen O'Connor

Recent advances in data science and digital epidemiology have unlocked an unprecedented amount of data for analysis, and uncovered previously unseen sex-specific patterns that point at marked differences in disease symptoms, progression and care that affect women of all ages. In 2016, the NIH published a guidance document1 and changed its policy for reviewing proposals whereby accounting for “sex as a biological variable” became a required and scorable aspect of the research strategy, highlighting that “an over-reliance on male animals and cells may obscure understanding of key sex influences on health processes and outcomes." Dr. Kathryn Rexrode, chief of the Division of Women’s Health at Brigham and Women’s Hospital, is quoted as succinctly stating the enormity of the problem: “without the inclusion of women, all the way through from basic research to clinical research, we can’t be sure we really have the right answers for 51 percent of the population.”

  • Contact: Graciela Gonzalez-Hernandez
  • Email: gragon at pennmedicine dot upenn dot edu

Achieving Trustworthy Biomedical Data

Co-chairs: Dennis Wall, Nicholas Tatonetti, Jan Liphardt, Bethany Percha, Serena Yeung, Peter Washington

Privacy and trust of data capture and sharing is an issue rising to the center of public attention and discourse. With the advents of large-scale academic, medical, and industrial research initiatives that collect personalized biomedical data from users, methods for providing sufficient privacy in biomedical databases and conveying a sense of trust to the user are crucial for the field of biocomputing to advance with the grace of the public.

  • Contact: Dennis P. Wall
  • Email: dpwall at stanford dot edu

Pattern Recognition in Biomedical Data for Discovery

Co-chairs: Brett Beaulieu-Jones, Christian Darabos, Dokyoon Kim, Shilpa Kobren, Anurag Verma

“Biomedical data” refers to the increasingly large corpus of machine-mineable data encompassing two similar, yet pointedly distinct fields: biology and medicine. In recent years, experimental and technological advancements in these fields have resulted in an unprecedented diversity of molecular omics data and longitudinal health record data available for analysis. Moreover, entirely new data sources such as social networking data, wearable technologies and environmental measurements have emerged and are relevant indicators of phenomena observed across both biology and medicine. Creative and sophisticated integration of these datasets promises the opportunity to further biological knowledge and understanding of disease and ultimately to advance our ability to more holistically detect and treat disease and improve patient care. However, challenges stemming from limited data quality and standardization coupled with a dramatic increase in data size and required computational resources arise in pursuit of these goals. In this session, we are particularly interested in innovative approaches that utilize new or new combinations of biomedical data sources to address previously intractable questions. We will focus specifically on cutting-edge methods aimed at pattern discovery in biomedical data

  • Contact: Brett Beaulieu-Jones
  • Email: Brett_Beaulieu-Jones at hms dot harvard dot edu

What about the environment? Leveraging multi-omic datasets to characterize the environment’s role in human health

Co-chairs: Kristin Passero, Shefali Setia Verma, Kimberly McAllister, Arjun Manrai, Chirag Patel, Molly A. Hall

The environment plays a key role in the development of disease. However, it remains a challenge to characterize and analyze potential environmental risk factors; the data is arduous to collect, has immense breadth, and exposures vary over time. This session focuses on environmental health research aimed towards the advancement of technologies, techniques, and infrastructure designed to better understand the role of the environment in human health.

  • Contact: Kristin Passero
  • Email: kxp642 at psu dot edu

Biocomputing and AI for infectious disease modelling and therapeutics

Co-chairs: Gil Alterovitz, Wei-Lun Alterovitz, Gail H. Cassell, Lixin Zhang, A. Keith Dunker

This session explores new computational approaches to this timely domain, such as the 2019-nCoV- released genomic sequences along with transmission networks, among others. The large number of bacteria, viruses, fungi, and other microorganism genomes that are available along with clinical implications of observed mutations, make these particularly amenable to development of novel computational methods. Genomics technology and bioinformatics have shown to be critical tools to help understand and solve these complicated issues ranging from understanding the process of infection, diagnosis and discovery of the precise molecular details, to developing possible interventions and safety profiling of possible treatments. By combining information at multiple scales, new insights may be gained as well. Researchers focusing on tool, pipeline and algorithm development will be encouraged to submit papers to this session.

  • Contact: Gil Alterovitz
  • Email: ga at alum dot mit dot edu

Computational Challenges and Artificial Intelligence in Precision Medicine

Co-chairs: Olga Afanasiev, Joanne Berghout, Steven Brenner, Martha L. Bulyk, Dana Crawford, Jonathan H. Chen, Roxana Daneshjou, Łukasz Kidziński

Rapid advances in sequencing and ‘omics technologies as well as increasing data streams from electronic health records (EHRs) and imaging data are opening up new vistas of personal biomedical and health data. In parallel, machine learning and other novel approaches have revolutionized our ability to analyze and find patterns in the multi-dimensional data generated by these modalities. Achieving the promise of precision medicine will require applying state-of-the-art computational tools to integrate and interpret the large volumes of data being generated. We welcome submissions along two tracks: (1) computational tools and methods for analyzing and interpreting genomics and multi-omics data and (2) machine learning tools for understanding disease and clinical outcomes.

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