This year marks the 33rd annual conference on Intelligent Systems for Molecular Biology (ISMB) and the 24th European Conference on Computational Biology (ECCB). At this year’s meeting, being held in Liverpool, UK, the International Society for Computational Biology (ISCB) is proud to present the Accomplishments by a Senior Scientist Award to Dr Amos Bairoch in recognition of his leadership and innovation in the development of bioinformatics resources and infrastructure. While Dr Bairoch has been responsible for truly transformative advancements in bioinformatics, his interest in science started in childhood when his father gave him a book on astronomy. Bairoch recalls being fascinated by the contents of the book, sparking a deep interest in space and space exploration. Around age 10, he began collecting articles about the Apollo missions, following the space program with keen interest, and by age 12, Bairoch wanted to become an astronaut. That dream lasted until high school where he discovered that, at the time, a career as an astronaut required a military pilot background—something he wasn’t keen on pursuing. Nevertheless, space and space exploration still fascinated Bairoch, so in university, he turned his attention to exobiology. The topic was gaining traction in the United States through figures like Carl Sagan, but it had not yet been established as a formal field of study in Switzerland or elsewhere in Europe. Not to be deterred from his passion for space exploration, Bairoch shifted his focus once again to biochemistry and mathematics, believing the two subjects to be an essential foundation for studying the chemical basis of life. It was his study of biochemistry converging with an interest in computers that ultimately brought Bairoch to the field of computational biology and bioinformatics. Bairoch’s first steps on the path of computational biology began with a TRS-80 that his father, an economic historian, bought to run statistical analyses. Bairoch started writing programs to help his father conduct these analyses, which allowed him to experiment and explore the capabilities of computer programming. Around this time, he was reading papers from the labs of Rodger Staden and Richard Roberts, both of whom were developing tools for DNA and protein sequence analysis. These tools, however, were built for mainframe computers—the kind only available at major research institutions. But Bairoch saw another possibility: bringing sequence analysis to personal computers, making this type of analysis much more accessible. By the end of his undergraduate studies, he brought the idea of sequence analysis on personal computers to Dr Robin Offord, a protein chemist interested in protein modification. Offord was intrigued by Bairoch’s idea and agreed to supervise him for his master’s thesis and later his PhD, though Offord recognized that he couldn’t fully assess the computational aspects of the work for the PhD project. Thus, he encouraged Bairoch to find a co-advisor with expertise in computational biology which led to Bairoch reaching out to Dr Jean-Michel Claverie at the Pasteur Institute in Paris. Claverie agreed to join the project as a co-advisor and every few months, Bairoch would travel to Paris to discuss his progress. Though their early meetings were focused on his PhD work and next steps for the project, their discussions broadened beyond the PhD itself, especially as Bairoch’s focus shifted toward developing what would become Swiss-Prot. Although Swiss-Prot wasn’t part of the original PhD project plan, it quickly became the centerpiece of his work, ultimately delaying the completion of his degree. Nevertheless, his advisors remained fully supportive, with Offord encouraging Bairoch to incorporate Swiss-Prot into the final thesis. Without the staunch support of both advisors, Bairoch’s pioneering contributions to bioinformatics might never have taken shape. There have been several influential people Bairoch has had the pleasure of working with, but when asked about the most influential mentors, he credited three key people with helping shape his academic research and bioinformatics career. Notably was Dr Robin Offord who was one of the first people to support Bairoch’s idea of bringing sequence analysis to individual labs. Beyond the direct support from Offord, Bairoch later came to learn that Offord had sheltered him from harsh comments from other faculty regarding his PhD project at the University of Geneva. Most of the faculty felt there was no place for computers in life sciences, voicing concern that making a whole project surrounding this was a waste of time that could be better spent on research that wasn’t just “playing around with a computer.” Offord pushed back against that narrative, advocating for the value of computational approaches in biology. He even enlisted colleagues in the UK—where computational biology was gaining more traction—to help make the case. For Bairoch, the greatest lesson he learned from Offord was a model of mentorship that taught him that a professor’s role is not only to conduct research, but to help students achieve their goals. Without that early advocacy, Bairoch might never have found his way into bioinformatics. A second key mentor in Bairoch’s career was his PhD co-advisor, Dr Jean-Michel Claverie, who provided intellectual guidance, reviewing Bairoch’s research and helping shape his approach to computational biology. Claverie not only provided bioinformatics insights that Offord was not fully able to provide, keeping Bairoch moving in the right direction with his PhD work, but he also provided camaraderie and meaningful discussions. Finally, Bairoch named Doug Brutlag as a key mentor in his bioinformatics career. Brutlag, the creator of IntelliGenetics, a company developing software to analyze DNA and proteins, recognized the potential of Bairoch’s software (PC/Gene) and was instrumental in arranging its commercial distribution, the royalties of which allowed Bairoch to hire a research team to help him with the development of Swiss-Prot. Brutlag introduced him to the concept of technology transfer and provided an understanding of the commercialization of bioinformatics tools. Bairoch’s own mentoring style was shaped by these key players in his career as well as by the unique circumstances of his work in bioinformatics. As Swiss-Prot expanded, his most significant mentoring impact has been through training biocurators, scientists responsible for annotating and maintaining high-quality biological data. In the absence of formal or standardized training programs in the field, Bairoch trained biocurators using an apprenticeship-style approach, where new curators learned through hands-on annotation work and received regular feedback and guidance. With this approach to training new biocurators, Bairoch said that his mentoring style is similar to Offord’s. Namely, doing all he could to grant young researchers maximum creative freedom, while encouraging senior scientists, himself included, to step back and empower the next generation. Bairoch’s early research career was highly independent, but as Swiss-Prot and the team around him grew, he had to transition from being a working scientist to being a research leader. He could no longer just be a colleague or collaborator—he had become a manager. With no clear roadmap for this transformation, he welcomed advice from scientists and management professionals and, over time, Bairoch learned that leading a large-scale research initiative required hierarchical structures, project management, and administrative oversight. Recognizing that bioinformatics required such specialized support, Bairoch helped establish the Swiss Institute of Bioinformatics (SIB), an institutional framework designed to support the long-term stability of bioinformatics research in Switzerland. The establishment of SIB meant that Bairoch’s role became less about individual research and more about ensuring the infrastructure and the team were successful, so he brought in Lydie Bougueleret to oversee administrative functions such as hiring and grant writing—tasks that had become too overwhelming to manage alongside scientific work. Through this transition, Bairoch learned to delegate leadership responsibilities and focus on building a system in which others could thrive. When asked what the most unexpected findings have been in his research, Bairoch had quite the unique answer! He said that there were no specific findings, but that what had intrigued him was the evolution of biological discoveries and how those discoveries impacted what software needed to be and do in order to keep up. For instance, Swiss-Prot started as small project and became the world’s leading protein sequence database used in biology and medicine. The management of its rapid growth required unexpected adaptations to make the product more widely available. Another unexpected direction of his work was the accidental creation of Cellosaurus. While working on neXtProt, a database for human proteins, it came to Bairoch’s attention that there was no central database for cell lines, so he began a small side project to create such a resource, and this grew into Cellosaurus, a widely used resource for cell line information—and another example of how research and its supporting software continues to evolve. This ongoing evolution—not just of databases, but of entire scientific fields—has remained a source of fascination for him. Bairoch said he was honored to be the recipient of the Accomplishments by a Senior Scientist Award. While he good-naturedly joked that a “young investigator” award would have been preferable—since senior scientist sounds like he’s at the end of his career!—he recognizes that this award is a reflection of his lifelong dedication to computational biology and bioinformatics and is truly grateful for the role he’s been able to play in shaping the field.

The ISCB Innovator Award is presented annually to a leading scientist who not only makes progressive contributions to computational biology but who also consistently pursues unexplored directions in the field. At this year’s, 33rd Annual Intelligent Systems for Molecular Biology conference and the 24th European Conference on Computational Biology, the International Society for Computational Biology is honored to present the Innovator Award to Dr Fabian Theis. Theis tracks his interest in science back to childhood, stemming from his love of building elaborate LEGO constructions—a passion that took a transformative turn around age 9 when he received an early personal computer. Unlike modern PCs, however, the PC Theis received did not have any games, but he saw that as a problem to solve and learned programming to create his own. This challenge of constructing games on his PC sparked a fascination with the logic, rigidity, structure, and algorithmic thinking involved in computer science. The fascination with logic and structure only deepened his passion for computer science, but it did little to spark an interest in biology. Theis recalls hating biology in school because of how messy and unpredictable it was. Biology did not have the clear, logical flow and firm laws of subjects like math and physics. It was not until the late stages of his PhD that Theis came around to biology. Theis realized that when computational methods and approaches were utilized, they could bring structure and order to occasionally chaotic biological data. This shift in understanding ultimately led Theis to the field of computational biology where he applied machine learning to analyze biological complexity to reveal patterns and insights that might be missed by traditional experiments. Theis credits several key mentors with shaping his academic and professional journey. Chief among them is his PhD supervisor Elmar Lang under whose guidance Theis shifted from a PhD in mathematics to one in biophysics. Through Lang, Theis transitioned from theoretical to applied research, gaining exposure to statistical learning, signal processing, and machine learning applications, and discovering the value of scientific publications along the way. Following his PhD, Theis set up a junior lab in Munich after being hired by Hans-Werner Mewes who had set up the first bioinformatics course in Munich. Mewes was instrumental in helping bridge the gap between computation and biology for Theis, helping him get into the field on a larger scale and connecting him with collaborators in biology. Friends and colleagues have also been important to Theis’s growth in computational biology. He noted Aviv Regev and Dana Pe’er as influential voices in the field, recalling that Pe’er’s Innovator Award Keynote address at ISMB/ECCB 2023 blew him away with the way she told the story of her research, weaving it together in a compelling narrative, demonstrating the importance of storytelling in science. Theis has also learned a lot from mentor and friend Matthias Tschöp, the current head of Helmholtz Munich. Under Tschöp’s guidance, Theis gained valuable experience in organizing large scientific teams and played a key role in establishing the Helmholtz Computational Health Center, which he now directs. The center has grown to include more than 40 PIs and over 400 scientists focused on AI-based computational biology and biomedicine. Last but not least, Theis highlighted his students as some of the greatest influences on his career, bringing fresh perspectives and innovative ideas to the lab. He shared that mentoring them and helping guide their academic journeys is what keeps him motivated each day because their curiosity and creativity continue to push his research in new and exciting directions. Theis recalls the transition from post-doc to principal investigator (PI) as being hard for him because he had to let go of direct problem-solving and doing everything himself. As a post-doc, he was used to working independently and solving problems quickly. As a PI, he’s had to be hands-off with problem-solving when it comes to his student’s projects, recognizing that it’s more beneficial for them to struggle a little and develop their own approaches without too much interference. Over time, he’s found that having different people working on problems in different ways not only benefits the students but also demonstrates trust in his team and ultimately helps to scale the impact of the lab’s work. When it comes to being a mentor, Theis credits much of his development to open conversations with peers—especially those who are one step ahead in their own careers—and to learning from the best practices he observed around him. Rather than assuming he had all the answers, Theis valued listening, adapting, and learning from others to shape a mentorship style focused on collaboration, communication, and interdisciplinary training. An example of this mentoring style is Theis’s “Y model” approach to training PhDs. Each PhD student has one mentor focused on computational and methodological skills, and one mentor focused on biological and experimental applications. This collaborative training ensures that each student has a well-rounded understanding of both theory and practice. Theis also strongly encourages students to explore topics of interest before settling on a single focus for their research, noting that completing a PhD can be a bit of a scary thing, but he aims to make sure his students have fun during that time, too! He wants them to have the freedom to experiment, take risks, and develop their own research identity. One of the most unexpected successes in Theis’s research career came when Alex Wolf, a post-doc in his lab, proposed improving the previously slow and inefficient single-cell analysis process through automation. Specifically, Wolf suggested they develop a structured framework for handling and analyzing the data, and from this suggestion, ScanPy was created. ScanPy is now a widely used standard for such analysis and has been downloaded over 5 million times! The experience in developing this software reinforced for Theis the importance of software engineering in scientific research which has since shifted his focus to prioritizing computational tools as a crucial part of biological discovery. In addition to the creation of ScanPy, he also reflected on the early days of whole-cell modeling in the 2010s, which at the time fell short due to limited data and computational resources. Today, however, advances in AI and data integration are reviving these approaches, making predictive whole-cell models far more feasible. Currently, Theis is most fascinated by foundation models in biology—these models are like large language models, but for biological data. His team is exploring how these models can be used to make predictions and guide experimental design with the hope that the foundation models will ultimately optimize not only how scientists interpret experimental output but also how they design experiments. This concept of models being continuously refined by new data and creating self-improving research frameworks is an exciting avenue that Theis is keen to explore! Theis said he was “honored and very humbled” to be named the 2025 ISCB Innovator Award recipient and to be recognized alongside some of the stars in the field. He also emphasized that this was not an award he earned on his own, but that it reflected the work carried out by his entire team, highlighting the “talented, fantastic students [he has] the honor of working with.”

The ISCB Overton Prize is awarded to a scientist for their significant contributions to computational biology. This year, at the 33rd annual conference on Intelligent Systems for Molecular Biology and the 24th European Conference on Computational Biology, the International Society for Computational Biology has the pleasure of honoring Dr James Zou with this award! Long before his career in computational biology, Dr James Zou was drawn to the challenge and logic of mathematics. This fascination began in elementary school, when he took part in math competitions and discovered that solving math puzzles was like a game—challenging, but fun! These early experiences with logical problem-solving laid the foundation for a lasting interest in mathematics. During middle school, a classic science fiction series gave Zou’s interest in math a new direction. When Zou read Asimov’s Foundation Trilogy, he was captivated by the premise that a mathematician could model the behavior of entire societies and predict the rise and fall of civilizations. The idea that math could be so influential sparked a growing interest in science and showed him the power of applying mathematical models to real-world problems. The love of math and science accompanied Zou into his undergraduate years in university. Because of this interest, he took part in a semester abroad program which brought him to Budapest, Hungary. It was in a course he took while in Budapest that Zou was first introduced to computational biology, where he studied the textbook The Biological Sequence Analysis by Durbin and colleagues. The focused study on such an influential book helped shape Zou’s understanding of computational biology and bioinformatics, especially with how computational models could be applied to DNA sequence analysis. Around 2010, Zou began studying for his PhD at Harvard, where he focused on computer science, algorithms, and machine learning. However, after reading about the groundbreaking discovery of Yamanaka factors—transcription factors capable of reprogramming mature cells into a pluripotent state—Zou’s research interests expanded to include biology. The idea that cells could be reprogrammed to an earlier state astonished Zou, prompting him to ask, “How is this even possible?” This curiosity led him to explore the use of computation and machine learning in uncovering the mechanisms behind Yamanaka factors and improve the efficiency of reprogramming experiments. Zou considers himself fortunate to have learned from many mentors throughout his career, crediting three groups of people who have significantly shaped his growth as a researcher. First were his early academic advisors, two of which stood out for their lasting influence: Dr David Parkes, a computer scientist and AI researcher, and Dr Brad Bernstein, a biologist specializing in epigenomics. Zou admired Parkes’s ability to think deeply about complex questions and identify problems whose solutions would have the greatest societal impact. Zou says that Parkes was someone from whom he learned a great deal and whose example he strives to follow in his own career. From Bernstein, Zou was introduced to the biological mechanisms behind cellular reprogramming and epigenomic regulation. Bernstein’s encouragement to engage in experimental work broadened Zou’s scientific perspective, strengthening his approach as a computational biologist. As he advanced in his career, Zou also learned a great deal from senior collaborators, especially during his early faculty years at Stanford. Among them, Dr Howard Chang—now Chief Scientific Officer at Amgen—Dr Tom Montine, and Dr Anne Brunet were particularly influential. Co-advising students alongside Chang, Montine, and Brunet allowed Zou to bridge his computational expertise with their wet lab research. These collaborations enriched his perspective of how computational tools could directly impact biological discovery and shaped the way he approached interdisciplinary work. Zou also credits his own students as important mentors. He sees them not only as trainees but as vital collaborators who bring fresh ideas and challenge him to explore new directions. One such student was Dr Ruishan Liu, whose research integrated genomic data with electronic health records to predict patient responses to cancer treatments. Collaborating with Liu, and many others, has been a continual source of learning for Zou, pushing his lab’s research into unexpected places. With all the lessons he’s learned from his own mentors and throughout his career, Zou likens his mentoring philosophy to a camping trip: It’s the job of the camp leader to find the most suitable place to camp and provide the tools needed to set up a good campsite, but when it comes to exploration of the surrounding area, the camp leader should let the campers take the lead. That is to say: As a mentor, a principal investigator (PI) should identify the most promising topics of research with interesting questions to explore. They should make sure their students are well-equipped to explore those questions and navigate the research landscape, stepping in to help clear obstacles when necessary. Most importantly, though, the PI should encourage their students to take the lead and explore ambitious questions beyond their immediate research area if they have a new idea or unanswered question they want to explore. In his lab, Zou has a system where any potential new PhD student he takes into his research group does a short-term “rotation” where they work directly with him on a mini project for two to three months. This process helps both Zou and the student determine whether their research styles, tastes, and interests are compatible before a long-term commitment to a PhD project. Zou says that this focused and deliberate process of bringing students into his lab has enabled him to recruit students from diverse research backgrounds and interests, including computer science, statistics, and medicine. From this position of PI and mentor, Zou says he’s become more focused on the broader societal impact of research, considering how the research and methods developed in his lab can directly contribute to bigger questions. Though he still researches foundational biological questions, considering the broader impacts of research has led him to prioritize projects with translational applications. Reflecting on his research, Zou mentioned a surprising discovery in 2020 when exploring whether tissue images could be used to predict gene expression in different tissue neighborhoods. A PhD student in his lab was keen to explore this question more closely and ultimately found that there were hundreds of genes whose expressions could be accurately predicted from morphology of tissues as seen in simple tissue images. This breakthrough in spatial biology demonstrated that imaging data could be computationally mapped to gene expression profiles. What’s more, this finding led to further research in Zou’s lab on spatial motifs—the way that cells arrange themselves in local patterns in tissues—which are predictive of how patients respond to immunotherapies and different cancer treatments. These findings have led to a continued fascination and excitement about mapping cellular environments across tissues and how integrating data from imaging, genomics, and proteomics will lead to a better understanding of human biology. In addition to spatial biology, Zou is currently interested in developing AI scientist agents who can help make biomedical discoveries. Specifically, he’s developing virtual labs where AI “professors” and AI “students” work together to solve different scientific problems, like designing new molecules and proteins and analyzing different computational biology tasks. In fact, these AI lab systems have already been applied to designing antibodies for COVID variants, with experimental validations showing very promising outcomes, demonstrating real-world potential! When asked how he felt about being named the ISCB 2025 Overton Prize award winner, Zou said that he’s “incredibly grateful, honored, and humbled” to be the recipient of the award and that it’s “quite surreal” to be following in the footsteps of so many of his scientific heroes who have previously won the Overton Prize! He also emphasized that the award isn’t just a personal achievement but is a recognition of the students, collaborators, and mentors he’s worked with throughout his career who’ve made every day of doing research inspiring and fun.

Every year, the International Society for Computational Biology (ISCB) presents the Outstanding Contributions to ISCB Award in recognition of a society member’s contributions to ISCB through their leadership, education, and service. This year, at the 33rd Annual Intelligent Systems for Molecular Biology (ISMB) conference and the 24th European Conference on Computational Biology (ECCB), ISCB is proud to present the Outstanding Contributions to ISCB award to Dr Lucia Peixoto! What began as a simple suggestion from a fellow PhD student would soon spark Peixoto’s long-standing leadership within ISCB. Prompted by that encouragement, Peixoto attended the 2007 Student Council Symposium (SCS), held ahead of the main ISMB conference in Vienna that year. Attending the symposium proved to be a turning point: not only did the SCS make the larger conference feel less intimidating, it also introduced her to a vibrant community of peers and marked the beginning of her long-standing involvement with ISCB. At the close of the symposium, when the chairs, Nils Gallenburg and Manuel Corpas, asked for volunteers to help organize next year’s event, Peixoto raised her hand without hesitation. Her offer to volunteer quickly turned into a request for her to chair the 2008 symposium. Though she had never chaired a symposium before, she was up to the challenge. This “say yes and figure it out later” approach was only the beginning of her leadership within ISCB. In 2009, she co-chaired the SCS symposium in Stockholm, and, that same year, became the SCS representative to the ISCB Board of Directors. While she enjoyed being part of ISCB and the SCS, she noted a distinct lack of diverse lived experiences within the community, particularly of scientists and students from underrepresented regions. This observation, in conjunction with her deep motivation to help people achieve their full potential regardless of background or location, played a crucial role in shaping her motivation to stay involved with ISCB. Being born and raised in Uruguay, Peixoto knew firsthand how difficult access to an international conference could be and she wanted to change that. She wanted everyone, no matter where they were from, to have access to the same experiences and opportunities she’d had with ISCB. So, in her role as SCS representative to the ISCB Board of Directors, she helped organize the first ISCB Latin America conference in 2010, which took place in her hometown in Uruguay, to bring an international conference to the global south. When asked about the role she sees ISCB fulfilling to support the computational biology community in the future, Peixoto emphasized hope, strength, belonging, and the importance of ISCB as a supportive force. She sees ISCB standing as a source of strength and resilience, especially at a time when the value of science and scientific institutions are being tested. A strong advocate for data-driven decision-making, Peixoto hopes ISCB will continue to affirm that everyone belongs in computational biology and that all ideas are valued equally, recognizing that diversity of thought and background drives scientific excellence. More broadly, Peixoto highlighted that scientific societies have a responsibility to lead with clarity and conviction, ensuring that their actions reflect their values to help build a more inclusive future for the field. Service, both within ISCB and beyond, has been a source of deep fulfillment as well as a source of inspiration for Peixoto’s research. Within ISCB, Peixoto was the founding chair and is the current co-chair, of ISCB’s Equity, Diversity, and Inclusion committee. In this role, she continues to focus on bringing data-driven approaches to improving and promoting equity and inclusion in science, a responsibility about which she is very passionate. Beyond ISCB, Peixoto’s research in neurodevelopmental disorders has been profoundly shaped by her engagement with patient communities. Her interactions with patients and their caregivers have been extraordinarily meaningful and have shaped her research in unexpected ways. A defining experience exemplifies how these interactions have shaped the direction of her research: Early in her career, Peixoto went to an open meeting on neurodevelopmental disabilities where parents, caregivers, and patient advocates were in attendance. She had been trying to decide on her research focus and while many papers were being published about the “hot” topics in the neurodevelopmental field, she thought, why not get the opinion of a non-scientist on what’s most important to study. When she posed this question to Geraldine Bliss, a parent at the meeting and the founder and president of cureSHANK (https://www.cureshank.org), the response was immediate: “sleep.” That simple, one-word answer shifted the entire trajectory of Lucia’s research, leading her to explore how sleep disturbances affect gene expression in the brain and their role in neurodevelopmental disorders. Her work continues to focus on the intersection of sleep, neurodevelopment, and autism, a path shaped by these community interactions. Peixoto is also passionate about STEM outreach, which she conducts through her involvement in public school programs and the Girl Scouts of North America. She loves speaking to school-aged children about science and especially enjoys speaking to young girls, motivating them to pursue STEM careers. Altogether, these experiences have reinforced a core belief for Peixoto: listening to and engaging with diverse communities, including non-scientists, is crucial to conducting meaningful, impactful research. In speaking about what service opportunities she would recommend junior scientists or trainees seek out, Peixoto said that it’s important for people to pursue the opportunities that bring them joy because personally unrewarding service can quickly lead to burnout. She encourages younger generations to pursue a holistic experience in their scientific careers, noting, “I want to dispel the myth that doing service stands in opposition of doing great science.” To be a great scientist, Peixoto says, isn’t just about valuable research and scientific contributions; it’s also about being a great mentor and giving back to your community. Peixoto feels deeply honored to be the recipient of the 2025 Outstanding Contributions to ISCB award since ISCB has been such an integral part of her academic career. She hopes that this award helps demonstrate to others in the field, no matter where they are in their career path, that science and service can co-exist, and she hopes, too, that this encourages more scientists to engage in service and that true excellence is about more than publishing papers.

Each year, the Overton Prize is awarded to a scientist for their significant contributions to computational biology. This year, the International Society for Computational Biology (ISCB) has the pleasure of honoring Dr Martin Steinegger with this award at the 32nd Annual Intelligent Systems for Molecular Biology (ISMB) conference being held in Montreal, Quebec, Canada from July 12 to 16. While Dr Steinegger is well entrenched in the field of computational biology these days, his path to this field wasn’t planned. In fact, when asked how he had become aware of computational biology, he said it was completely by accident! Steinegger recalls his interest in computers being a foundational aspect of his academic journey. Specifically, it started when, as a child, he received a computer with a digital operating system and was very focused on getting games running—as any young child would be! This early experience helped fuel the spark of problem-solving. Despite his interest in computers and technology, however, school was a different and more complicated story, and the path to university was anything but direct. Growing up in a small Bavarian town and being dyslexic, Steinegger was placed in an educational track that didn’t give him the option to pursue a university education. Instead, he was put on a path for job placement and was told that with his education, his options for the future were going to be limited. Instead of accepting limited options, Steinegger left the restrictive German education system behind to enroll in a technical school in Austria and later turned mandatory military service into civil service which allowed him to transfer to a professional school for business informatics. From there, he ended up working as an IT consultant, and though he enjoyed this position, it wasn’t the challenge he anticipated. Leveraging his professional schools’ education, and encouraged to do so by a former partner, Steinegger pursued a university education to find the challenge he was looking for. Even with a strong technical background, abundant experience, and a desire to learn and to challenge himself, the decision to attend university was surprising to Steinegger’s father. He had expected that Steinegger would continue in the working world as he had done because that was a predictable, consistent, and stable career path, and he was concerned about the future his son would have following university. Nevertheless, Steinegger forged ahead! He does recall, however, that the beginning of his university career was fraught with feelings of inferiority due to his non-traditional academic career. Because of these feelings, he was determined to prove that he could handle the challenges of university. During the early semesters of his studies, he initiated ambitious independent research projects aimed at predicting the effects of mutations in the human proteome, alongside his close friend and co-worker, Dr Milot Mirdita. The longer he attended university, his self-doubt waned, and the projects and challenges met became inspiration to explore and discover more instead of a means to prove himself. Eventually, Steinegger found himself working on a project that was investigating protein changes and whether such changes could be considered severe. The ability to answer this question was a big computational challenge and was a significant influence in setting him on a course to computational biology—because when there’s a challenge, he takes it on! Despite the higher visibility projects like MMseqs2, Foldseek, and ColabFold, Steinegger is particularly partial to a project her surmounted during his PhD: Linclust, a clustering algorithm used to address the problem of slow clustering methods of metagenomic data that got even slower with the increasing size of datasets. With the relative speed of the Linclust algorithm, Steinegger was able to help address multiple questions that had previously been untouched, from clustering proteins in the billions to assembling genomes to detecting incorrectly labeled DNA in our biggest genomic databases. When asked how the findings and outcomes from his work influenced his later research pursuits, Steinegger said that he knew the Linclust algorithm, or something similar, could be used in fields outside of protein study or computational biology. His hope is that researchers refrain from thinking only in terms of fields of study, effectively limiting themselves to a confined space, but instead think in terms of opportunities. Other fields have big questions waiting to be answered and a similar algorithm could be just the solution! While he’s excited to answer as many big questions as possible, both in and outside of computational biology, currently, Dr Steinegger’s research interests remain focused on protein structures. Now more than ever, researchers have access to millions of high-quality predictions that could shed light on the structural universe. Protein structures can be viewed on an unprecedented scale; they can be analyzed in ways they were never able to be analyzed before. On a larger scale, there’s also the ability to more readily conduct comparative analyses and investigations of the natural world, exploring questions of how proteins and other machinery influence life on Earth. When it comes to training a new generation of computational biologists, Dr Steinegger’s personal training and mentorship experiences, as well as the shift to being a Principal Investigator (PI), have influenced his own methods of training and mentoring students. Conducting research from the position of a PI has meant that, instead of only having to consider his own strengths in research, Steinegger now must consider the strengths and skillset of other people and has to be ready to adjust the questions being asked by young researchers to the skills they have and where he seems them growing. Steinegger has taken an approach of optimistic support with new research projects for his students. That is, the potential pitfalls of research questions aren’t the initial focus. Instead, as a student’s research project is being built out, they start with the basic framework of an idea and consider how the question can be answered with the tools available, but also consider opportunities for new avenues of investigation. The stance of making the basic idea work before considering pitfalls and complications comes from Steinegger’s own research experience, especially during his PhD. He vividly remembers having great, thought-provoking discussions with his advisor; discussions that challenged him to look at things differently, more simply. It was during this time that he really solidified his idea of “get it running first, then we can make it complicated.” He is long since learned the lesson that perfectionism from the outset is not always possible or plausible. The drive for perfection sometimes stops forward momentum in research, and Steinegger tries to instill the idea in his own students that perfection isn’t the goal, but exploration and scientific discovery are! When asked how it felt being named the 2024 Overton Prize winner, Steinegger summed it up by telling us that it “felt surreal” and that there are a lot of other people he saw as deserving of this award. He’s humbled to be winning this award and hopes to continue making strides to help answer the big questions in the field of computational biology.

The ISCB Innovator Award is presented annually to a leading scientist who not only makes progressive contributions to computational biology, but who also consistently pursues unexplored directions in the field. This year, at the 32nd Annual Intelligent Systems for Molecular Biology conference (ISMB), the International Society for Computational Biology has the honor of presenting the Innovator Award to Dr Su-In Lee, the recent winner of the 2024 Samsung Ho-Am Prize, the Korean Nobel Prize. Dr Lee’s affinity for math and science began from a young age, in large part thanks to her father and his background in shipbuilding engineering. His profession fascinated her because of the output of his work—designing large ships capable of staying afloat and moving quickly through water—an output requiring a deep understanding of several areas, including physics and fluid mechanics. But her father’s influence on her interests didn’t stop there! Not only did he play a key role in fostering her love of science, but he was instrumental in her love of mathematics, too, having taught her math, like calculus and geometry. These lessons, in turn, nurtured her love of mathematical patterns and theorems. From this love of patterns and theorems, Lee’s interest in math grew, leading her to immerse herself in the world of AI research during her undergraduate education in Korea. Her curiosity in this relatively new field led her to conduct her undergraduate thesis—for which she won the Best Undergraduate Thesis Award from Samsung—on developing a deep neural network (DNN) that could recognize handwritten digits. From this project, Lee was inspired to use DNNs and other similar models to understand the bigger questions surrounding human cognition and how the brain works. When Dr Lee started her PhD at the Stanford AI Lab, however, her research pursuits changed course once more. The emerging microarray technology being used in molecular biology and genetics, and the accompanying computational challenges presented therein, captivated her, effectively marking the beginning of her exploration into computational biology. When she accepted a faculty position at the University of Washington, the scope of Lee’s research broadened to include AI applications for clinical medicine, where AI interpretability and transparency were focal points. Presently, her lab operates at an intersecting point of the ABC fields upon which the future of medicine hinges: AI, biology, and clinical medicine. Looking back on her academic career, Lee notes that she has been surrounded by many inspiring and influential people. There have been so many influential people, in fact, that narrowing down a singular most influential figure is a difficult task. But Lee does have a few people that come to mind who have significantly impacted her academic career toward and within computational biology. One very influential individual for Lee was her undergraduate thesis advisor Dr Soo-Young Lee at the Korea Advanced Institute of Science and Technology. Dr Soo-Young Lee introduced her to the world of AI and subsequently to the intricate world of deep neural networks. This early introduction to AI laid much of the groundwork for Lee’s future endeavors! When Lee continued into her PhD, her advisor Dr Daphne Koller, a prominent AI researcher, was responsible for introducing her to the field of computational biology. In fact, Lee was one of Koller’s early students to study AI and biology, specifically exploring AI’s usage and usefulness in addressing biological questions. Following her PhD years, Lee cites Dr Aviv Regev, a former professor at MIT and the Broad Institute, as having been an encouraging and inspirational figure since Lee’s early faculty career as a computational biologist and setting an extraordinary example of leadership and the importance of interdisciplinary collaboration. When it comes to her research, Lee has stated that one of the most unexpected findings so far was the “critical importance of model interpretability and explainability.” Specifically, it was the depth of the impact of interpretability and explainability that became increasingly evident as work in Lee’s lab progressed. Because of this revelation, Lee’s research group took a unique approach to developing clinical AI models by prioritizing interpretability, leading to the development of SHAP, an influential and widely used explainable AI framework. Delving into AI interpretability ultimately reshaped Lee’s research pursuits. What was previously a side topic of interest became an exciting focal area of research! With respect to current areas of interest, Lee has said that her research into AI auditing frameworks stands out most. This work utilizes explainable AI to audit existing AI models to uncover processing flaws. A large part of this research is due to the discovery of concerning trends in COVID AI prediction and classification models. Specifically, AI’s predictions were getting things wrong because it was relying on shortcuts. Lee says she’s drawn to the area of AI model auditing because it underscores the importance of understanding the reasoning processes of different AI models so that such models aren’t blindly used and implicitly trusted. Having a more thorough understanding of the reasoning processes is something that, while useful in clinical medicine, can also be useful in many other disciplines including computational biology. From the point of view of a Principal Investigator (PI), Lee has gained a deeper understand of research as a whole through the more broad perspective of research that a PI has to take. For instance, a PI must oversee project direction, manage resources, recruit collaborators, find funding, promote the lab’s research, and guide trainees—the latter point being the most important part of the job, according to Lee! Before coming to computational biology, Lee initially pursued computer science and electrical engineering. The transition to biology and medicine solidified for her the importance of being willing to tread into unfamiliar territory and embrace new challenges by learning unfamiliar concepts and tackling problems in a different field. These lessons are ones that Lee tries to instill in her own students. She encourages them to explore different areas of research and to take a fearless approach to learning new things! When students express doubt in themselves or their knowledge in biology, she reminds them that they are biologists and that a fearless approach to learning, both within your own field of study and in new ones, can lead to some of most meaningful scientific contributions. On being the recipient of the 2024 ISCB Innovator Award, Lee said, “it’s an incredible honor for me,” and that she’s very grateful for this recognition. She noted, too, that there are so many outstanding researchers in the field that it seems a nearly impossible task to select just one, but she is truly humbled to have been chosen as this year’s winner.

This year, at the 32nd annual conference on Intelligent Systems for Molecular Biology (ISMB) being held in Montreal, Quebec, Canada, the International Society for Computational Biology (ISCB) is proud to be presenting the Accomplishments by a Senior Scientist Award to Dr Tandy Warnow in recognition of her significant contributions to the ever-growing field of computational biology. From a young age, early in her elementary school days, Dr Warnow has loved math. For her, it was something wonderful, fun, and beautiful. She very distinctly remembers a moment from her childhood where the flame of this curiosity was fanned: A teacher talked about counting using 10 as the base simply because that’s how many fingers we have. But what if we had 12 fingers, or six? Instead of being “10” years old in base ten, she could be “14” years old in base six! She’d be a teenager already! Warnow recalls this moment, and the questions that bubbled to mind, as a “wonderful, fun thing” that really helped her to discover just how cool math could be. As she got older, the lessons about asking questions and being curious about the world stayed with her. Over time, her education-related interests expanded to include science—with a particular interest in black holes during high school—and the many topics covered therein. But even though science fascinated her, math was still her favorite subject, the love for which continued to be nurtured by her teachers; specifically, by her 9th-grade algebra teacher, Mr Bernstein, who she still remembers with great respect. What Warnow liked most about Mr Bernstein was that he was tough, but fair, expecting a lot from his students and encouraging them to solve problems they thought they couldn’t. He pushed them to do well in a subject that people often found difficult, and Warnow appreciated the intellectual challenge and the belief that he had in his students to do well. Support from teachers and mentors turned out to be a defining feature during Warnow’s academic career. Many professors during her undergraduate years believed in her and provided an educational environment that fostered her love of math and science, as well as the growth and exploration of the limits of her own understanding. When she moved into graduate study, continuing her journey with mathematics, her perspective of the types of questions math could answer was shifted when her PhD advisor, Gene Lawler, introduced her to algorithmic problems in evolutionary biology, that had what Warnow called “beautiful graph theory versions.” This pivot in perspective continued during her years as a post doc where her advisors, including Michael Waterman, were continuing with this direction of analysis and examination, focusing on the important questions in biology that math could address, and emphasizing utility in biology over mathematical elegance and beauty. As a Principal Investigator (PI) heading her own lab, Dr Warnow continued to focus on theory from the point of view of her training as a mathematician. That is, until she hired Ken Rice as a post doc, an unexpected inspiration to a new direction for her research questions. Rice introduced Warnow to simulations and the importance of a data-driven approach to big questions. When Warnow transitioned from a theory-only approach and began analyzing data more thoroughly, she discovered that the insights that came to light were very different than those uncovered when working solely within a theoretical framework. With this new perspective, Warnow’s focus changed to encompass theory and experimentation, as the necessity of combining the approaches to gain a comprehensive understanding of a problem and its potential answers had become abundantly clear. Such exploration led her to turn her attention to phylogenetic tree estimation from a theoretical and data-driven point of view. Some of what fascinated Warnow about this area were the differences in outcomes between theory and data. Focusing only on theory meant that predictions of the accuracy of phylogeny estimation methods, for example, were sometimes based on subtly incorrect interpretations of the theory. Incorporating simulations of evolution allows for a refinement of theoretical frameworks and better understanding of the theory itself. This change in how she approached her work, by combining both theory and simulations, led to a breakthrough in her understanding of methods, models, and theory—and has changed how she approaches all her research questions. Warnow’s current research is related to phylogenetic networks and other evolutionary questions that extend beyond a simple tree (e.g. hybridization, horizontal gene transfer)—areas she’s found particularly fascinating because of the rarity of feasible tools for analyzing this kind of biological data. In her work with these subjects, she’s discovered how difficult it is to review and interpret such data, which has highlighted a large gap between aspiration and reality in this realm of study. But this gap, and the unanswered questions found there, are a large part of her inspiration to forge ahead with this work! Warnow’s shift in status from PhD to post doc to PI has brought about an expansion of her focus and responsibilities to not only include unexplored research questions, but to incorporate the “administrative” side of science as well, like the process of securing grants and navigating the politics of academic publication. Beyond those tasks, however, her approach to training and mentoring students has been significantly shaped by the meaningful interactions with inspirational people in her academic career, and by her own research interests and experiences. For instance, following her own experience with seeing the importance of combining theory and data to achieve more thorough, cohesive answers to research questions, she’s adamant that students in her lab understand they can’t only focus on theory; they have to incorporate data, too. When speaking about mentoring students, Warnow stated that “mentoring relationships work [best] when there’s trust in both directions.” As a PI and mentor to those in her lab, she aims to be a support system for students to help them learn to be researchers. She described an experience she had as an undergrad when Turing Award winner Dick Karp talked about the ups and downs of research, and how he weathered the discouraging moments. She shares these lessons with her students, encouraging them to keep going, and to focus on how much fun research is, rather than focusing on trying to be successful. We should not, as researchers, equate poor research outcomes with who we are as people. It is just not true! To help them avoid getting caught in that negative headspace, she empowers her students to believe in themselves and their research and helps them develop coping strategies to handle setbacks. Above all, just as she received during her formative research years, she supports and believes in her students. She teaches them to ask good questions and to learn to interpret data effectively. Importantly, she also holds high expectations for them, mentoring her students toward mastery. Finding out that she was the recipient of the 2024 Accomplishments by a Senior Scientist Award, Dr Warnow said, “I was really shocked.” She had not thought she would be considered for this award, given the growing popularity of machine learning as a tool for problem-solving; a tool upon which her research does not focus. She was quite certain she would not have been in the running since her current focal area, phylogenetics, is not a central research area within the larger field of computational biology but an outlier. Though she was not expecting it and was truly surprised by the recognition, ISCB is more than pleased to be honoring her with the award this year!

Each year, ISCB presents the Outstanding Contributions to ISCB Award to recognize a society member’s contributions to ISCB through their exemplary leadership, education, and/or service. This year, the outstanding service award is being presented to Dr Scott Markel. Dr Markel’s involvement with the International Society for Computational Biology began in the early 2000s when the Society was growing steadily around the Intelligent Systems for Molecular Biology (ISMB) conference. At one of the earliest ISMB conferences Markel attended, he took part in a Birds of a Feather meeting that helped to connect people in computational biology. This is when Markel met B.J. Morrison McKay, the executive director of ISCB at the time, who gave a presentation about the Society. Here is where Markel learned about the committees within the Society. As his first foray into the organizational and administrative side of ISCB, Markel first got involved with ISCB’S Publications and Communications Committee and “things snowballed from there.” But they certainly snowballed in a good way, seeing Markel become more deeply involved in the Society’s activities as ISCB continued to grow! While he is stated that he is less hands-on with Society activities these days, Markel was previously the Secretary for ISCB’s Board of Directors, a position that he held for a decade. In addition to his role as Secretary and his initial involvement with the Publications Committee, Markel has also been involved in the Finance Committee, Fundraising Committee, and the Nominations Committee, the latter of which, he said, gave him a unique perspective of the society. With so much involvement in so many places within the society, it is clear that Markel’s influence is not only invaluable, but widespread. When asked what service opportunities he had found most gratifying in his position with ISCB, Markel said that something he loved the most was being able to be part of “the magic behind the scenes of helping organize the smooth running of the Society.” The most notable service provided to the Society according to Markel, however, was heading the search committee that found the current CEO of ISCB, Diane Kovats, when McKay was stepping down. In addition to helping find McKay’s replacement, Markel was involved in outlining the rules of Society governance so there was a strong system in place. In particular, he was involved in introducing term limits for officers and board members, and in introducing the President Elect and Past President roles, which aid with a smooth transition between presidents and helps to ensure a clear transfer of knowledge. All in all, Markel has been a key component in bringing structure and continuity to the society’s leadership and administration, and his efforts will be appreciated for as long as the society continues! For junior scientists and trainees looking for service opportunities in the society or in the world of science in general, Markel says: “Be curious. Experience things. And don’t be afraid to share your experiences.” He suggested three specific areas that trainees can consider when it comes to seeking out such opportunities: the technical/science side, the service side, and the educational side. When it comes to the technical/science side of things, Markel suggests starting with things like reviewing papers and growing into bigger roles by volunteering to organize and chair special sessions at scientific meetings. From the service perspective, the suggestion is to find a committee or Community of Special Interest (COSI) to join and see what opportunities are available within that community. Markel urges students and trainees to be willing to volunteer, take on new challenges, and experience as much as possible. From the educational side of things, the advice from Markel is for students to think about how people helped them when they were coming into the academic world and how they might be able to do something similar to pay it forward. How did mentors help foster growth? What lessons did they share that cannot be learned in the classroom? Markel also suggests involvement in panel discussions to share experiences with things like getting published and writing grants. He also says to write articles! If there’s specialized information a student has to share, they shouldn’t shy away from doing so. Do not be afraid to put your knowledge out there! Markel did offer another sage piece of advice for trainees looking for service opportunities: Do not commit yourself to something if you do not have the work ethic and follow-through to go along with it. The role of ISCB in supporting the computational biology community “will depend on where those in charge of the society lead it,” says Markel, but he sees it going in a great direction, especially since ISCB already has some very strong pillars of support for the computational biology community. For instance, the jobs board and career center information provided on the website and at ISMB’s career fair help students see what is possible outside of academia. This is invaluable in supporting the next generation of scientists, providing exposure to opportunities they might not have known about. The Society is also a great bridge between academia and industry, clearly connecting the two in a research area that is growing rapidly, and that Markel foresees is going to be big. An area where Markel sees ISCB already doing great things but where he hopes to see the society continue to grow is in providing an infrastructure to support the academic community within computational biology, and sharing and spreading knowledge within this area. Namely, ISCB can continue to support and provide access to COSIs for students and trainees to find like-minded researchers and the society can also continue to strengthen its publication arm, promoting the cutting-edge research happening in the field and continuing in ISCB’s mission of spreading the science!