Dear Alberto,

Many thanks for the kickoff text. I will try to produce a couple of direct comments. 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.
In any

case, it was nice hearing from a biomedical researcher in the wet lab.
Best wishes--Pedro

On Tue, 06 Mar 2018 21:23:01 +0100 "Alberto J. Schuhmacher"  wrote:
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
procedure designed to test hypotheses. Experiments are an important tool of the scientific method.
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 
profound revolution.
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
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.
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
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)?
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
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?
Best regards,

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)
Phone:(+34) 637939901

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