AI/ML/Bioinformatics in CF
Can you briefly talk about your research on AI/ML/bioinformatics approaches in cystic fibrosis (CF)? and what is cystic fibrosis?
"In my research, I focus on accelerating and improving the diagnosis of antibiotic resistance in patients with cystic fibrosis (CF) by using advanced DNA sequencing techniques and machine learning. Normally, when an infection occurs, sputum is coughed up and analysed by growing bacteria on a culture plate. This process is time-consuming, as it can take several days to more than a week to determine which bacteria are present and which antibiotics will be effective. My approach circumvents this step by directly extracting and sequencing DNA from the sputum sample. By analysing the sequences using deep learning tools, I can quickly and accurately identify resistance genes without having to grow bacteria in a lab. This saves considerable time, with the literature showing that the most optimal procedures now yield results in as little as six hours, compared to the traditional method that can take several days. This significantly speeds up diagnosis, which can be crucial for the treatment and well-being of CF patients."
"Cystic fibrosis (CF) is a serious genetic disorder that affects all mucus-producing organs, such as the lung, but also the gastrointestinal system, for example. It is characterised by a defect in a channel that normally helps pump water from mucus-producing cells to keep the mucus fluid. This defect leads to an accumulation of tough mucus in the lungs, making it difficult to fight infections and remove bacteria. This condition not only affects the lungs, but can also affect other organs, such as the intestines and reproductive system. Thanks to recent advances in medication, the life expectancy of CF patients has improved significantly, from about 20 years at the beginning of this century to the expectation of 70 years with the latest treatments. These advances highlight the importance of continuous improvement in diagnostic and treatment methods to further optimise the quality of life for patients."
How does your collaboration with Amsterdam UMC contribute to the development and application of AI/ML/bioinformatics approaches in your research?
"In my current role, I work closely with both Amsterdam UMC and Vrije Universiteit (VU), with my background in machine learning and artificial intelligence playing a central role. Although my education was not originally focused on these technologies, I gained a lot of experience during my internships and graduation projects. This has led to a special collaboration where VU provides valuable insights and data that support my projects at AMC, while the findings from AMC feed back to VU."
"My co-supervisor and supervisors, including Jacqueline, are essential in this collaboration. Jacqueline brings in a lot of expertise and helps with the design and execution of the studies. We also maintain regular contact with other researchers working on the microbiome, which makes for a fruitful exchange of ideas. This interaction between basic and clinical research provides a unique opportunity to turn theoretical knowledge into practical applications, which benefits both my research and clinical patient care. This integration of different worlds is particularly inspiring and greatly enriches my work."
Why did you choose to do this research? How did you get involved?
"During my master's I was looking for an internship and found a project on antibiotic resistance in CF. This appealed to me because I have always been interested in genetic disorders and bacterial research. My interest in the microbiome and working with large datasets led me to this research. When I decided to pursue a PhD part-time, Jacqueline saw the opportunity to set up a joint project that encompassed both my interests and expertise. That was the perfect opportunity for me to further develop my passion for genetics and data analysis."
What findings and conclusions do you have so far with your research? What is the future outlook for CF patients?
"We have promising results with our first study on the use of DNA analysis for antibiotic resistance. The aim is to test this method more widely with a larger dataset. If the findings are further confirmed, we may be able to accelerate diagnostics and thus improve treatment methods. This is expected to ultimately contribute to a better quality of life for CF patients, with faster and more targeted treatment of infections. We are still at the beginning, but the prospects are promising."
AI/ML/bioinformatics in Education Research
What does your research on AI/ML/bioinformatics in education involve?
"I am currently working on a project comparing online and offline education within a pharmaceutical practicum. We have looked extensively at interaction patterns between students and lecturers, such as who talks to whom and how these interactions proceed in terms of questions, answers, and overall participation. We also surveyed students about their experiences, such as whether they felt safe and motivated during the sessions, and whether they felt they learned as much in an online environment as in a physical classroom. What was striking is that despite the online nature of some sessions, students still perceived them as useful and even enjoyable. This is interesting because although many students complained during COVID about the lack of physical presence, they are starting to see the added value of online sessions, especially when it comes to preparatory classes."
"When analysing the data, I use methods that I previously applied in bioinformatics. The beauty of this is that although the type of data is different, the techniques for discovering patterns and relationships are similar. For example, we found that an online environment leads to 20% fewer interactions between students on average. However, when we dive deeper into the data and include other factors, such as the nature of assignments and group dynamics, we find that the quality of interactions is not necessarily worse. These insights show that although the amount of interaction decreases, the essence of what is learned and shared is often retained."
"What I find particularly interesting about this research is that it allows us to analyse complex, human-centred issues using techniques normally used in controlled laboratory settings. In a social, educational setting, it is much harder to control for all variables, but by using advanced analytical tools, we can still draw valuable conclusions. This research thus not only provides insight into the effects of online versus offline education, but also helps us understand which factors influencelearning and student-teacher interaction the most."
Does this research have a connection to your research on AI/ML/bioinformatics approaches in CF? Or is it separate?
"My work in education research and CF research are largely separate at the moment, but there is definitely influence between the two. For example, the analytical skills I am now learning as part of my education research I am also applying in my CF research. Thus, I am developing practical skills that are valuable for both fields. In addition, qualitative research in education is much more common, and I use that knowledge again when setting up new research projects within the AMC, such as formulating open-ended questions and interviews, something that is not always obvious in a beta-oriented environment."
"What I really appreciate about my work is how it allows me to build bridges between different research areas. My training in both bioethics and biomolecular sciences has taught me to look at issues from multiple perspectives. This versatility helps me translate between theoretical knowledge and practical applications, which benefits both my education and CF research. This makes my work challenging and varied."
To what extent do you collaborate with Amsterdam UMC in your educational research?
"For my education research, I do not work directly with the Amsterdam UMC. This project falls under the VU. Nevertheless, the experience I gain in data analysis and teaching innovations can be indirectly valuable for my collaboration with the UMC. The knowledge and skills I gain in one research area can influence my work in another. However, I do regularly share what I have done and my findings that may be applicable to my other research. In addition, I proudly share my milestones that I have achieved."
Do you have a mission and vision for the research you are working on?
"My personal mission is to bridge the gap between different research disciplines, such as STEM and alpha sciences, and show how they can reinforce each other. I work on blurring traditional boundaries between these disciplines to arrive at new, multidisciplinary insights. I also have a personal vision and that is that multidisciplinary research, where we also collaborate with disciplines such as social sciences, leads to valuable new insights and innovations. This helps us develop a more integrated approach that promotes both scientific and societal progress."
"In my CF research, I focus on improving care for people with cystic fibrosis by developing personalised care using new technologies such as the metagenome. My mission is to create a system that provides quick and effective answers about resistance, not only for cystic fibrosis, but also for other lung diseases. In addition, my vision is to use technology and data analysis to develop more personalised and effective treatments. Through multidisciplinary collaboration, I want to achieve breakthrough results not only within lung diseases, but more broadly in medicine, and bridge the gap between research and practice."