The Test Kitchen at Hattie B's Hot Chicken
When Roland finishes his lecture he will head to Hattie B’s in Nashville, TN for a unique interview.
How You Can Participate As An Audience Member
The questions for the interview will come from the comment section below.
You can leave a comment or question, and also be sure to up-vote the other questions you want asked.
Then, questions with the most up-votes will be asked during the Hot Chicken Interview at Hattie B’s, which is being recorded.
The deadline to submit questions for the interview is 6:00 PM on Wednesday, October 20th, 2021.
About Prof. Dr. Roland Eils
Prof. Dr. Roland Eils is founding director of the Berlin Institute of Health’s Digital Health Center at Charité ̶ Universitätsmedizin Berlin and director of the Health Data Science unit at the Medical Faculty of Heidelberg University. Before, he was founding and managing director of Heidelberg University’s Systems Biology center BioQuant and Head of Division “Theoretical Bioinformatics” (B080) at the DKFZ in Heidelberg. His group has delivered significant contributions to the field of cancer genomics and systems biology. Since 2017 Roland Eils is member of the Organizing Committee of the Human Cell Atlas initiative and Coordinator of the HiGH-Med Consortium. He has published over 370 publications cited over 49000 times resulting in an h-index of 99 (source: google scholar, last visited 2021-09-15).
Another question on ML and AI; do you see an ideal model that simply does not need any human intuition to predict outcomes, i.e. predict development of disease or response to therapeutic. If so, how far along are we from seeing this model, and is the limitation with computation power, data availability, or something else?
Side question on AI; How much of the data you model with is ‘dark matter’ i.e. functionally unknown such as some non-coding DNA regions. Because, you know, when AIs take over at least they can figure out some unknowns.
John, are you a student? can you come to Hattie B’s at ~3:30 pm, Charlotte avenue, for interview? thanks VQ
I have multiple questions regarding different projects in Eils Lab and about the Systems biology field as well.
1. The Eils lab webpage describes Omni-phenotype genetic models to study many health-related phenotypes such as BMI and blood pressure. Environmental factors affect these phenotypes too so how do these models account for these factors? Are these models considering the environmental factors effect on the genes and hence the phenotype, if so, how is this effect accounted for in these models mathematically?
2. Research in Biology is witnessing a dawn of a new era with large amount of data about systems (mostly) accessible, but I personally tend to get overwhelmed when I see a lot of data associated with a study and often doubt if I am over-interpreting the data and seeing what is not there or is an exceedingly small effect. Did you feel the same when you started in this field? How did you deal with this skepticism? What are the practices one should consider implementing to make their computational methods highly rigorous and easy to follow for others?
3. As a female just starting in the field of computational biology, I have often witnessed underrepresentation of female in it. In your experience would you agree this to be the case, and have you implemented any practices to change that, or can you describe some practices that you consider will help increase female representation?
What was your motivation for founding the Berlin Institute of Health’s Digital Health Center?
With more people unwilling to share personal data and medical records in light of COVID19, how do you plan to overcome that obstacle even with machine learning (ML) methods in place if people are not willing to want to share that data in the first place? Or even if it is shared through blanket consent by receiving care from a hospital, what are the potential legal ramifications by conducting ML methods even with privacy filters in place?
Systems biology sometimes is criticized for having very complex analyses and information that comes from data-driven studies (such as gene transcription networks), which can be difficult to make sense of the information. Have you found helpful ways to engage with people who do not understand this perspective and make systems biology research clearer for people to understand?
What are the challenges in taking a large amount of -omics types of data and making enough sense of them to narrow down a study to specific disease mechanisms? How do you approach overcoming such challenges?