Call For Papers, Abstracts and Demonstrations

Pacific Symposium on Biocomputing

Big Island of Hawaii - January 4-8, 2025

The paper submission deadline has passed. A PDF of the PSB 2025 proceedings is available here.

The thirtieth Pacific Symposium on Biocomputing (PSB), will be held January 4-8, 2025 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 to present significant original research, demonstrate computer systems, and facilitate 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

Papers must be submitted to the PSB 2025 paper management system.

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 (complete and unpublished manuscripts) to bioRxiv and/or medRxiv, these are online archives and distribution services operated by Cold Spring Harbor Laboratory for preprints in the life sciences and health sciences respectively. If you choose to submit your preprint, please ensure that the deposit is made at the time of the paper submission deadline rather than upon acceptance to avoid clashing with bioRxiv and medRxiv policies.

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

Important dates

Paper submissions due (absolute deadline): August 1, 2024 11:59PM PT
Notification of paper acceptance: September 9, 2024
Final paper deadline: October 1, 2024 11:59PM PT
Abstract deadline: December 2, 2024 11:59PM PT
Meeting: January 4-8, 2025

Paper format

Please reference the PSB paper format template and instructions.

Papers must be submitted to the PSB 2025 paper management system.

The accepted file format is PDF (Adobe Acrobat preferred). 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, title page with author list, or references) in our publication format. 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).

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 2025 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 and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface

Session Chairs: Jonathan Chen, Roxana Daneshjou, Dokyoon Kim, Joseph D. Romano, Fateme Nateghi Haredasht, Geoff Tison

In a wave of disruptive technology, large language model chatbots are giving access to interactive systems capable of surpassing humans in clinical reasoning, while generative image models blur the distinction between fabricated vs. real information and intelligence. This session will showcase cutting edge research invoking such methods to enhance patient care through clinical decision support, monitoring tools, image interpretation, and triaging capabilities, even as in-depth studies are needed to assess the impact and implications of such systems on human lives.

  • Contact: Jonathan Chen
  • Email: jonc101 at stanford.edu

Earth Friendly Computation: Applying Indigenous Data Lifecycles in Medical and Sovereign AI

Session Chairs: Keolu Fox, Krystal Tsosie, Kaja Wasik, Alec Calac, Alexander Ioannidis, Eric T. Dawson

The rapid expansion of artificial intelligence (AI) across fields - particularly with the advent of powerful Large Language Models (LLMs) and the application of AI in human health - is generating vast quantities of data, consuming massive amounts of energy, and leaving behind significant environmental footprints. This growth poses a potential risk of exacerbating the climate crisis, which may, in turn, have major downstream impacts on global health.

This session will focus on the innovative integration of Indigenous data lifecycle models that respect the principles of data as a sacred entity and relation, with the growing field of medical AI, particularly in precision medicine and public health. We aim to foster a dialogue on how computational methods, rooted in Indigenous wisdom, can not only enhance the accuracy and fairness of AI in healthcare and other fields but also promote the conservation of environmental resources in computational practices.

  • Contact: Keolu Fox
  • Email: pkfox (at) ucsd.edu

Precision Medicine: Multi-modal and multi-scale methods to promote mechanistic understanding of disease

Session Chairs: Yana Bromberg, Steven Brenner, Hannah Carter

Technological advances in high-throughput omics technologies have made it possible to develop a new class of biomarkers that predict patient drug responses, susceptibility to diseases, and other medical outcomes. Toward the goal of precision medicine, methodological advances are needed to translate such biomarkers to the clinic as well as provide mechanistic insight as to their clinical utility. We welcome all submissions relevant to this exciting and growing area of research.

  • Contact: Hannah Carter
  • Email: hkcarter at health dot ucsd dot edu

Translating Big Data Imaging Genomics Findings to the Individual: Prediction of Risks and Outcomes in Neuropsychiatric Illnesses

Session Chairs: Peter Kochunov, Li Shen, Paul M. Thompson, Zhongming Zhao

The session will solicit presentations to describe challenges and solutions of translating Big Data Imaging Genomic findings into actionable insights for personalized medicine, facilitating informed clinical decisions at the individual level. 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.

  • Contact: Yizhou Ma
  • Email: yizhou.ma at uth.tmc.edu

Overcoming health disparities in precision medicine

Session Chairs: Kathleen Barnes, Harris Bland, Francisco De La Vega, Todd L. Edwards, Keolu Fox, Alexander Ioannidis, Eimear Kenny, Rasika Mattias, Bogdan Pasaniuc, Jada Benn Torres, Digna R Velez Edwards,

This session seeks to advance computations methods and data science approaches to overcome disparities in precision medicine and public health by addressing racial, ethnic, and gender disparities across biomedical research, patient, provider, and health system levels, which are influenced by a mix of social, psychosocial, lifestyle, environmental, and biological factors. This year's emphasis will include intersectional research approaches that examine how various social identities and categories—like race, gender, class, sexuality, age, ability, and ethnicity—interact to shape individual experiences in healthcare and systemic inequalities. Emphasizing the role of 'Big Data' and the Electronic Health Record, the session will discuss challenges in computational approaches to intersectionality, particularly the capture and analysis of Social Determinants of Health (SDOH) and environmental risk factors. Strategies being developed to address these challenges include enhanced data collection through large population-based cohorts, multi-pronged approaches within large EHR populations, and advanced geocoding to assess environmental impacts in conjunction with clinical data. These efforts are essential to overcoming existing limitations in capturing complex and dynamic information such as transgender health issues, where traditional data collection methods fall short.

  • Contact: Francisco De La Vega
  • Email: francisco.delavega at stanford.edu