Abstract: Hypnograms are a schematic representation of sleep,
depicting its different stages through the night. This work
presents a new strategy for classifying these data, based on CCCBiclustering
for time series. By exploring local sleep patterns, one
might be able to find significant ones which are characteristic to
a given group of patients. The developed method is applied on
a dataset consisting of hypnograms for six different pathologic
populations and a control group. Although the preliminary
results are not so satisfactory, future work is discussed, showing
The development of applications that manage large text
collections needs indexing methods which allow efficient retrieval
over text. Several indexes have been proposed which try to reach a
good trade-off between the space needed to store both the text and the
index, and its search efficiency.
Self-indexes are becoming more and more popular in the last years. Not
only they index the text, but they keep enough information to recover
any portion of it without the need of keeping it explicitly.
Therefore, they actually replace the text.
My program of research focuses on the integrated use of neurocomputational models and empirical techniques – most notably brain imaging – to investigate the neural bases of learning and cognition in healthy subjects and the disruption of these processes in psychiatric disorders. This talk will present three examples in which this integrated approach has been crucial to arrive at novel insights that would be out of reach for more classical approaches.
About SING (http://sing.ei.uvigo.es/) @ University of Vigo
The Next Generation Computer Systems Group (SING, Sistemas Informáticos de
Nueva Generación) brings together a reduced number of researches with the
aim of developing intelligent models and deploying them in real
environments. The expertise of the members comes from different areas
related with previous research in developing symbolic, connexionistic and
hybrid AI systems, solving security problems, administration of networks,
e-commerce, VoIP, implementation of web applications and developing systems
In metabolic engineering or synthetic biology robust models with high predictive power are required. Constraints-based modelling methods such as metabolic flux analysis (MFA), flux balance analysis (FBA), elementary flux modes (EFMs) or extreme pathways (EP) have been widely used. The success of these methods is however conditioned by the many times insufficient mechanistic knowledge base. In this study, we built upon a previously developed hybrid constraints-based modelling method to develop E. coli models with improved predictive power.
It has recently become possible to record the EEG simultaneously with fMRI, providing whole-brain maps of the hemodynamic (fMRI) correlates of electrophysiological (EEG) activity. The combination of the EEG high temporal resolution with the fMRI high spatial resolution offers a unique opportunity for studying the spatio-temporal dynamics of brain activity noninvasively. Here, I will focus on the application of EEG-fMRI to the study of spontaneous epileptic activity in patients undergoing pre-surgical evaluation.
I will give an introduction to hybrid modeling methods for bioprocess and biochemical networks modeling. Hybrid methods combine parameter-free modeling with statistical modeling tools. They enable to blend mechanistic knowledge and statistical relationships into models with improved performance and broader scope.
Computational modelling of protein interactions (docking) is an important endeavour because protein complexes are difficult to determine by experimental methods alone. Nevertheless, computational prediction of protein interactions is no trivial task either and there is much to be done to improve the reliability of protein docking methods. Protein coevolution traces that are identifiable from the analysis of multiple sequence alignments can help predict protein interacting contacts and can be used to constrain the search space of constrained docking algorithms, such as BiGGER.
Systems biology provides new approaches for in silico metabolic engineering and drug development through the application of analysis, simulation and optimization methods for metabolic models. In silico modeling of cellular metabolism is divided between genome-scale stoichiometric models and small-scale kinetic models. While the former are analyzed using optimal assumptions predicting intracellular microbial fluxes and growth rates, the later are used for dynamic behaviour simulations. However, there is currently a separation between these two modeling approaches.
Subcellular location is an important property of proteins, carefully regulated
by the cells. To determine subcellular location on a proteome-wide scale,
fluorescent image data is most commonly used and a classification system is
employed for analysis. These systems assign each protein to one of a small set
of predefined location classes (typically the major organelles).
This is a limited representation of the underlying biology as proteins are
often in multiple organelles. I will present techniques that go beyond the