Many thanks for the kickoff text. I will try to produce a couple of direct
You have reminded me of the early 70's, when I first approached science. A
few computers had made their entrance in the university halls. During those
years, and for some decades to come, a new mantra was to be ensconced:
modeling, simulations. Thanks to computers, we had a fascinating new tool; a
mathematical machine that was opening a new window to the world of science,
equivalent to the telescope or the microscope in the scientific revolution.
Now, almost 50 years later, after having provoked their own "information
revolution" it seems that computers are more than a new tool. Dataism
coupled with artificial intelligence, deep learning and the other
techniques, have taken them to the command post, so that they are becoming
direct "agents" of the scientific progress. And this is strange.
They have already defeated masters of chess, of go and of other contests...
are they going to defeat scientists too? Are they the "necessary" new lords
of all quarters of techno-social complexity?
You have depicted very cogently the new panorama of biomedical research,
probably the mainstream, and I wonder whether this is the most interesting
direction of advancement. In some sense, yes (or no!), as it is where big
biomed companies, technological firms, and management establishment are
pointing at. It is easy to complain that they are leaving aside the
integrative vision, the meaningful synthesis that facilitate our
comprehension, the "soul" in the machine... But we have been complaining in
this way at least during the last two decades. So I really do not know.
Fashions in science come and go: maybe all of this is a temporary illusion.
Or a taste of the science of the future.
case, it was nice hearing from a biomedical researcher in the wet lab.
On Tue, 06 Mar 2018 21:23:01 +0100 "Alberto J. Schuhmacher" wrote:
procedure designed to test hypotheses. Experiments are an important tool of
the scientific method.
Dear FIS Colleagues,
I very much appreciate this opportunity to discuss with all of you.
My mentors and science teachers taught me that Science had a method, rules and procedures
that should be followed and pursued rigorously and with perseverance. The scientific
research needed to be preceded by one or several hypotheses that should be subjected to
validation or refutation through experiments designed and carried out in a laboratory.
The Oxford Dictionaries Online defines the scientific method as "a method or
procedure that has characterized natural science since the 17th century, consisting in
systematic observation, measurement, and experiment, and the formulation, testing, and
modification of hypotheses". Experiments are a
social issues to those that decide the rhythms of life and death.
“Information flow” is one the “supreme values” of this religion. The next
floods will be of data as we can see just looking at any electronic window.
In our case, molecular, personalized and precision medicine aims to anticipate the future
development of diseases in a specific individual through molecular markers registered in the
genome, variome, metagenome, metabolome or in any of the multiple "omes" that make up the
present "omics" language of current Biology.
The possibilities of applying these methodologies to the prevention and
treatment of diseases have increased exponentially with the rise of a new
religion, Dataism, whose foundations are inspired by scientific agnosticism, a
way of thinking that seems classical but applied to research, it hides a
Dataism arises from the recent human desire to collect and analyze data, data
and more data, data of everything and data for everything-from the most banal
experimental hypotheses themselves will arise from the observation of data
of previously performed experiments. Are we facing the end of the wet lab?
Is Dataism the end of classical hypothesis-driven research (and the
beginning of data-correlation-driven research)?
The recent development of gigantic clinical and biological databases, and the
concomitant progress of the computational capacity to handle and analyze these
growing tides of information represent the best substrate for the progress of
Dataism, which in turn has managed to provide a solid content material to an
always-evanescent scientific agnosticism.
On many occasions the establishment of correlative observations seems to be
sufficient to infer about the relevance of a certain factor in the development
of some human pathologies. It seems that we are heading towards a path in which
research, instead of being driven by hypotheses confirmed experimentally, in
the near future
translation, natural language processing, social network filtering,
bioinformatics and drug design, where they have produced results comparable
to and in some cases superior to human experts. Will be
data-correlation-driven research the new scientific method for unsupervised
deep learning machines? Will computers became fundamentalists of Dataism?
Deep learning is based on learning data representations, as opposed to
task-specific algorithms. Learning can be supervised, semi-supervised or
unsupervised. Deep learning models are loosely related to information
processing and communication patterns in a biological nervous system, such as
neural coding that attempts to define a relationship between various stimuli
and associated neuronal responses in the brain. Deep learning architectures
such as deep neural networks, deep belief networks and recurrent neural
networks have been applied to fields including computer vision, audio
recognition, speech recognition, machine
Alberto J. Schuhmacher, PhD.
Head, Molecular Oncology Group
Aragon Health Research Institute (IIS Aragón)
Biomedical Research Center of Aragon (CIBA)
Avda. Juan Bosco 13, 50009 Zaragoza (Spain)
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