Nuno Barbosa Morais is a computational biologist. He holds a degree in Technological Physics Engineering from Instituto Superior Técnico and a PhD in Biomedical Sciences from the Faculty of Medicine of the University of Lisbon, having carried out international research for a decade at the universities of Cambridge and Toronto. Since 2015, he has led the Disease Transcriptomics laboratory at the Instituto de Medicina Molecular and teaches courses in Computational Biology at several Masters courses at the Faculty of Medicine of the University of Lisbon.
In conversation with José Maria Pimentel, Nuno identifies a series of challenges and obstacles to good science. The first challenges have to do with the great difficulty of Science as an activity: managing to understand the world (identifying “laws” in nature) with the data we have always partial and imperfect, and relying only on the mind of scientists — human and, therefore full of limitations and biases.
To counteract our cognitive limitations (and our own moral defects) an institutional architecture was created over time with a series of safety valves. For example, works are only published after being reviewed by other scientists, and science is done openly, so that we are always subject to our conclusions being invalidated by other researchers.
And, in order to decide what counts and what does not count as a scientific discovery based on such limited data, it was necessary to create a method and a framework of significance accepted by all. Statistical inference tests were instituted, the so-called hypothesis tests, the best known of which is the famous p-value.
However, these tests are just an indirect way of inferring conclusions (since it is never possible to be sure about our hypothesis to explain a certain phenomenon, the most these tests do is… reject the hypothesis that there is no phenomenon in the data… ).
Due to the limitations of our minds and these statistical methods, Science has always been a…complex activity. And in recent decades some changes have made these obstacles even greater. On the one hand, the system for publishing scientific articles has become increasingly competitive, generating incentives to publish impressive results, even if this means being less rigorous. On the other hand, science (in particular in the guest area, biomedical sciences) has become more complex and computerized due to the rise of so-called big data and the increased use of “bioinformatics” programs. This created additional challenges for those who use these tools without sometimes understanding them well.
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