New DDLS Fellow: Golnaz Taheri
The SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) continues to recruit outstanding early career scientists. Our latest Fellow, Golnaz Taheri (KTH), talks about using machine learning to identify significant genes and biomarkers in cancer and to predict drug interactions and side effects, in our latest Q&A-style article. Golnaz will be joining the DDLS Cell and molecular biology research area.
Golnaz has loved researching for as long as she can remember, and her favorite subjects were math and biology, which seemed a bit apart at the time. She researched and found out that she could get them both in computational biology. So, she started with the computer science part. During her bachelor’s, she became increasingly interested in extracting meaningful information from networks and graphs, which led to her master’s thesis on protein interaction networks. In her PhD, she explored another side of computer science with task scheduling networks for multiprocessors.
After completing her PhD, she realized that her passion, computational biology, could benefit a lot from the machine learning knowledge she had gained. Her postdoc at KTH/SciLifeLab on cancer biology and machine learning was a good match, and after that, she continued as a Senior Researcher there. Two years ago, she took on a new challenge as Senior Lecturer at Stockholm University, which gave her experience in pedagogy, teamwork, and management. Now she’s back at KTH/SciLifeLab, where she always wanted to be, running her own lab.
How do you think your expertise can contribute to the program?
This program focuses on data-driven life science. My interdisciplinary background as a computer scientist with expertise in AI and machine learning who has worked with large multi-omics biological datasets, becomes very well suited for solving life science problems. In other words, I can develop and apply the suitable machine learning-based solutions for life science challenges as my specialty is modeling complex biological problems, especially in cancer biology and drug interaction prediction.
Shortly describe your research in an easy to understand way.
My research focuses on computational biology and biological network modeling using machine learning tools. I work mainly on two topics, Cancer Biology and Drug Interaction Prediction.
Cancer Biology: I use Machine Learning to identify significant genes and biomarkers, especially in female cancers. The goal is to improve the understanding of these cancers and hopefully suggest therapeutic strategies to enhance the quality of life for women living with cancer.
Drug Interaction Prediction: Here, the aim is to develop a systematic framework for predicting drug interactions and side effects. This work is particularly important for improving the quality of life for elderly patients and those with complex health conditions who struggle with multiple diseases.
How do you think the program and interactions with the other DDLS-Fellows will benefit you?
The DDLS community is versatile and strong while sharing a similar scientific mindset. This is something unique and invaluable that makes me feel fortunate to be part of it, learning from senior researchers, exchanging experiences with other fellows, and of course gaining more insights into mentorship and leadership. The generous funding is another enabling aspect of it which has made me realize the dream of establishing a lab as a crucial step in my research journey. I’m excited for all that we can achieve to improve human life in this lab.
Name one thing that people generally do not know about you.
I’m very into human science. I read books and listen to podcasts related to psychology, philosophy, history and sociology. We have a book club with some of my friends that we meet regularly to discuss these subjects, especially through the books that we are reading.
Where do you see yourself in five years regarding the DDLS aspect?
I see myself as the group leader of a motivated, skilled, diverse, and interdisciplinary team working on data-driven life science challenges, creating an environment where creativity and collaboration thrive, while nurturing the new generation of data-driven scientists by sharing my experiences and knowledge gained throughout my journey. With my team, hopefully we can bridge the gap between research and clinical practice, working closely with clinicians, developing solutions that improve the quality of life for patients facing challenging health situations, such as cancer.
In one word, describe how you feel about becoming a DDLS-Fellow.
Joy!
