Baiba Vilne, a leading researcher at Rīga Stradiņš University (RSU), has been part of our team for six years and, since the beginning of this year, has held the position of tenure professor. She obtained a bachelor’s degree in biology from the University of Latvia, after which she received a scholarship from the German Academic Exchange Service (DAAD) and went on to study bioinformatics at the Technical University of Munich. She then remained there to complete her doctoral and postdoctoral studies.
She later decided to return to Latvia. At first, she worked at the national scientific institute BIOR, before being invited by professors to join RSU. Since 2019, she has been developing her own research group at RSU and, in her words, “had already been working towards the position of tenure professor”.
You spent 13 years in Munich and then returned to Latvia. What motivated you to come back?
In my application for the DAAD scholarship, I had already written in the justification for my participation that I was going abroad to study so that I could later return and give back to Latvia what I had gained. I did not return as quickly as originally planned, because interesting research opportunities arose, but during my postdoctoral studies it became clear: it was now or never. Before you begin building your own research group, you can still change direction; later, it becomes much more difficult to do so.
What is the research environment like in Latvia, at RSU and in Germany?
Overall, these environments are becoming increasingly similar. We are actively involved in various international research and infrastructure consortia. We also prepare project applications at the European level.
I have observed that our researchers are highly capable and already well integrated into the international research community.
This means that these environments are converging. One difference may be that in Germany there was more collaboration with the United States, whereas we cooperate more at the European level. The next step would be to broaden our reach and establish more partnerships worldwide.
Let us turn to your current research. Please tell us what it focuses on.
During my postdoctoral research, my work had a strong focus on coronary artery disease, and this topic has remained central to my subsequent research. The postdoctoral stage is when researchers lay the foundations of their careers, establish collaborations and tend to produce a comparatively larger number of publications.
I currently lead the Integrative Bioinformatics Group, and our work has two main directions. On the one hand, many other research groups in the life sciences require support in bioinformatics. For this reason, since returning to Latvia, our research topics have been extremely diverse. At BIOR, for example, they even included biofilms in water pipes, while during the COVID-19 pandemic we worked in the field of virology.
Many of our project partners are also neurologists, with whom we are currently preparing additional project applications. At the same time, we naturally continue to work with other partners, many of whom are cardiologists.
What all these groups have in common is that they collect very large and complex datasets.
For example, this may include genomic information, gene activity or the transcriptome, protein measurements or the proteome, the metabolome (metabolic indicators), the microbiome (measurements of bacteria and viruses), lifestyle indicators and environmental factors. The challenge is always to find the best way to analyse such vast amounts of data meaningfully within a unified system. This may involve identifying patient subgroups or discovering new biomarkers that could help predict the risk or progression of a disease at an early stage, or guide treatment.
At the same time, our work is much broader: bioinformatics is also an independent field of science, because new and improved methods must be developed that are best suited to each specific research question. Our task is to select and compare methods, as well as to develop new ones.
For example, when studying genomic information, we can use methods that help identify a possible cause of a disease.
If we assume that a particular disease is caused by specific changes in the genome, we begin the research from that point and gradually incorporate other data to determine how these changes might affect other biological processes in the body.
We also develop experimental designs, for example, to determine how an understanding of mechanisms gained from model organisms such as mice or rats can be transferred to humans, and how data from one study can be aligned with data from another.
In this context, it is worth recalling that bioinformatics is an interdisciplinary field at the intersection of biomedicine, mathematics, statistics and computer science. Our work involves drawing on what we need from each of these disciplines in the context of a particular study and bringing it together in a meaningful way.
So, do you work with data collected by others?
Yes, our collaboration partners usually approach us, and ideally the data have already been collected. Alternatively, we prepare joint project applications in which they plan to collect the data. In the latter case, we can advise which types of data would be meaningful from a bioinformatics perspective.
Please share one or two conclusions from your research that are useful to society.
I would prefer to answer more broadly. Many people have probably heard of the so-called Gartner Hype Cycle. Whenever a new technology, method or discovery emerges, it generates considerable interest both in society and among professionals, and very high expectations are placed on it. However, once the technology or method is put into practice, its limitations become apparent. This may be followed by a certain degree of disappointment, and only after this phase do we arrive at a more realistic perspective.
In my field, I can identify several such Gartner Hype Cycles. I would begin with human genome sequencing. When the Human Genome Project was completed in 2003, it was believed that we would soon be able to understand the mechanisms of all diseases and that the best diagnostic methods and treatments would become available within a short period of time. Later, however, it became clear that the genome sequence alone was not enough. We then concluded that other layers of data also needed to be collected, such as gene activity (the transcriptome) and proteins (the proteome).
We have now entered the next Gartner Hype Cycle, in which we understand that big data in itself is not yet information. We need to determine how all these data can be brought together, meaningfully integrated and interpreted.
Artificial intelligence is, of course, currently going through a Gartner Hype Cycle, and we are in the phase of heightened expectations. However, it is clear that this too will not be a magic wand.
We need to understand that we are moving through these Gartner Hype Cycles. We need an appropriate experimental design that helps us answer our research question. We must begin with very specific and precisely defined research questions, including from a bioinformatics perspective. We also need software tools and workflows that are reproducible.
You mentioned artificial intelligence. Does your work also involve training it?
We are now at a point when artificial intelligence, particularly AI agents, is indeed becoming dominant across all fields, including bioinformatics.
A new field known as agentic bioinformatics has emerged, and we are, of course, working in this area. This means that the role of the bioinformatician is changing.
In bioinformatics, each of us is building our own artificial intelligence agent, or agents, with the idea that in the future they could semi-autonomously take over certain functions, particularly routine tasks, for the purposes of automation. These agents can already help identify the most suitable tools, perform certain analyses and produce documentation.
Machine-learning methods have, of course, been part of our everyday work for quite some time. In a broader sense, this is one of the ways in which we integrate multi-layered data. We train machine-learning models using specific datasets so that the system can then identify biomarkers, such as metabolic indicators or genomic variations, that could help distinguish, for example, patients with coronary artery disease from a control group, namely people who have not been diagnosed with the disease in question.
However, I would emphasise once again that artificial intelligence is not a magic wand and close human oversight is still essential. The ability to ask the right questions is needed more than ever, and the results must be interpreted responsibly.
One of the projects in which RSU is involved under your leadership is ELIXIR. Please tell us about it.
ELIXIR is a European intergovernmental organisation in which member states bring together their research infrastructures within a single consortium. In modern science, we are increasingly collecting vast amounts of data from very large groups of individuals, and countries need support in managing these data, as well as standards for best practice and well-trained experts. ELIXIR provides these opportunities.
It is therefore building a network of people and infrastructures in which experience is exchanged, best practices are developed jointly and information about various training courses is brought together. RSU has taken on the role of coordinator within this organisation.
More specifically, we began this work a year ago by implementing Latvia’s national partnership and action plan within ERDF Project No. 1.1.1.5/3/25/I/014, “RSU Participation in the Horizon Europe Programme”. Our task is to help establish a nationwide network, or consortium, of institutions in Latvia. In this way, we are strengthening bioinformatics research infrastructure and expertise in Latvia.
You are a tenure professor, but before that you were a leading researcher at RSU. What do these positions have in common, and how do they differ?
Of course, my work has not changed radically. The greater difference was between postdoctoral research and leading a group. A postdoctoral researcher works more or less independently on their own research topic, whereas a group leader manages a team, prepares project applications and secures funding. They are, of course, also involved in the team’s work, although perhaps less in carrying out data analysis or developing software tools themselves, and more in coordinating data analysis, software development and the writing of publications.
I see the position of tenure professor as a step to the next level. It brings greater visibility and, of course, even greater responsibility.
Where I see a change in relation to the tenure professorship as a bioinformatician is that previously we mainly performed a supporting role: assisting collaboration partners, contributing to the preparation of their project applications and helping to analyse the data obtained. The tenure professorship adds a further responsibility to develop bioinformatics more purposefully and intensively as an independent field of science.
This means that we must define our own research objectives, prepare projects as coordinators and develop new methods.
How large is your team?
The size of the team changes depending on the project funding secured. At present, there are six of us, which is an optimal number. However, if we obtain additional funding, for example through major European-level projects, we can increase our capacity.
Is the team made up of RSU colleagues, or is it international and also includes representatives from other universities?
Our team mainly consists of people who have studied or are currently studying computer science. We also have medical engineers and people with a background in biostatistics. Sometimes we are joined by biologists who want to learn programming. RSU primarily trains medical professionals, and they are our collaboration partners, but they are usually not specialists working in bioinformatics. Building the team is therefore a challenge, and we need to recruit specialists both from other universities and, quite often, from abroad.
You have already mentioned the coordination involved in a researcher’s role, but what does it mean today to be a researcher who leads a project?
It is difficult for me to answer this question objectively, because I have only experienced what it means to be a scientist today (smiles). It is often said that being a scientist or researcher is not merely a profession; it is essentially a way of life. It is a way of looking at the world. This idea resonates strongly with me, because at heart a scientist is someone who remains curious, takes an interest in the world around them, asks questions and tries to find evidence-based answers. They may not stop at the first answer, but continue to question and compare. Sometimes, they must also acknowledge what we do not yet know. I would like to think that this aspect has remained unchanged over the years.
In my view, the role of a scientist today has become much broader and more complex. Whereas in the past a scientist may often have worked alone within a narrow field of research, today research is increasingly a team effort. That is certainly the case in our field.
Our work is based on interdisciplinary collaboration. We need to be able to work with people from different fields and understand one another.
At the same time, technology is developing very rapidly, and researchers—at least bioinformaticians—must be prepared to master new technologies within a very short period of time. As we have already noted, the development of research infrastructure is also becoming an integral part of a scientist’s work. Securing funding is, of course, an additional challenge. Researchers must be able to prepare interdisciplinary project applications, often need a broad understanding of innovation, and must also be able to explain clearly to society what we do and why.
How do you recharge so that you can continue advancing science?
For me, recharging means acquiring new knowledge, because I am a very curious person.
I love learning new things and gaining new knowledge, so I read extensively, not only within my immediate field of work. I also very much enjoy listening to podcasts about other fields, including other natural sciences, mathematics and physics. I am also interested in the social sciences and humanities. I am fascinated by the philosophy of science and even science fiction.
It may sound slightly ironic, but yes—outside science, it is more science that recharges me.
