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Public and Patient Involvement (PPI) is increasingly recognised as a valuable component of health research, yet there are no published examples of its implementation in computational biology. In this study, we detail how we embedded the perspectives of those affected by ovarian cancer into a biomarker discovery project. Throughout this process, PPI partners gave practical guidance on prioritizing research areas, developing study materials and contributing as co-authors on both patient information leaflets and this publication. To ensure this involvement was meaningful and inclusive, we prioritized three key strategies. Firstly, the research team placed significant emphasis on flexibility in order to facilitate accessible participation. We offered online and in-person formats, utilised explanatory videos, scheduled around PPI team availability, and held 1-to-1 “catch-up” sessions for new members. This emphasis on flexibility enabled PPI partners to contribute to the project on their own terms and helped to build trust with the research team. Secondly, we came up with ways to value PPI contributions beyond monetary reimbursement. PPI partners were compensated for their time and expenses; however, we also supported personal learning goals through “journal club” videos and interactive sessions with other researchers. These methods of appreciation helped to promote the individual goals of each contributor and ensure that the collaboration was mutually beneficial for both researchers and PPI partners. Finally, and importantly, we acted on the feedback received from our PPI partners and reported what changes we had made to our project. Input from PPI partners informed the selection of liquid biopsy types, widened the breath of the project to include multiple subtypes of ovarian cancer and influenced the development of patient information leaflets. When combined, these three tactics produced an inclusive PPI process that improved the relevance, applicability, and acceptability of the biomarker discovery study. While these principles are broadly applicable across PPI initiatives in general, we provide reflections on their importance in addressing the challenges of PPI in a computational context. In addition to offering a guide for researchers looking to develop more responsive and cooperative PPI research practices, our experience demonstrates the potential for impactful patient partnership in computational biology. Public and Patient Involvement (PPI) means working with patients and members of the public as partners in research, rather than doing research about them, without their input. PPI is becoming more common in health research, but it has not often been used in areas like computational biology, a field that uses computers and data to understand biological problems. In our case, we use computer-based tools to study tiny building blocks in the blood, known as molecules, that might help detect ovarian cancer earlier. In this project, we involved a group of PPI partners (people with personal or family experience of ovarian cancer) throughout a two-year study. They helped guide the scientific project by suggesting research questions to focus on, reviewing patient information materials, and helping to write this paper as co-authors. To make sure the partnership was meaningful and accessible, we took several steps. We made it easy for people to take part by offering online and in-person options, creating short videos to explain scientific ideas, and being flexible with timing. We also supported new members with 1-to-1 sessions to help them catch up with the larger group. We recognised PPI partners not just by covering their time and expenses, but by offering learning opportunities, like short videos explaining scientific papers and interactive sessions with researchers, so they could get more out of the experience. Most importantly, we acted on their suggestions. For example, the PPI team influenced what types of samples that we used to look for clues in the body that could point to ovarian cancer (markers), helped expand the project to include rarer types of ovarian cancer, and shaped the design of patient information leaflets. Overall, involving patients and the public in this way made our research more relevant, understandable, and useful. We hope that in this paper, by sharing what we did and how we did it, we can offer ideas for other researchers looking to build strong, respectful partnerships in projects that involve PPI in general, as well as those that use data and computer-based approaches to study disease.