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,
AJ
---
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)
email: ajime...@iisaragon.es
Phone:(+34) 637939901
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