Beyond
basic research, biology in general and biomedicine in particular
are increasingly and rapidly becoming data-based sciences, an
evolution driven by technological advances in image and signal
non-invasive data acquisition (perfectly exemplified by the 2014
Nobel Prize in Chemistry for the development of super-resolved
fluorescence microscopy), or high-throughput genomics, to name
just a few. In the Biomedical field, the large amount of data
generated from a wide range of devices and patients is creating
challenging scenarios for researchers, related to storing,
processing and even just transferring information in its
electronic form, all these compounded by privacy and anonymity
legal issues. The situation is not different in healthcare,
where electronic health records are becoming commonplace and new
possibilities such as remote home monitoring, or wearable
medical devices are likely to make an impact as part as the
ambitious p-Health, or 4-P (Predictive, personalized,
preventive, participatory) paradigm for medicine. Data-based
healthcare finds a paramount example in the current Institute
for Systems Biology (ISB, Seattle) "Hundred Person Wellness
Project"(*), a pilot in which 100 healthy individuals are
intensively monitored on a daily basis.
New
data requirements require new approaches to data analysis, some
of the most interesting ones are currently stemming from the
Computational Intelligence (CI) and Machine Learning (ML)
community.
This
session is particularly interested in the proposal of novel CI
and ML approaches to problems in the biomedical and healthcare
domains, with a non-exclusive focus on methods that overcome the
"black-box syndrome" by making models interpretable and thus
fulfil the usability requirements of most real medical
applications.
Topics
that are of interest to this session include (but are not
necessarily limited to):