INESC-ID   Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa
technology from seed


Knowledge Discovery and Bioinformatics
Inesc-ID Lisboa

A data mining approach to study disease presentation patterns in Primary Progressive Aphasia.

12/05/2014 - 14:30
12/05/2014 - 15:30

Nowadays the world is faced with an ageing population and the related challenges, as
healthcare issues given the current incidence of diseases more prevalent in elders, such as
neurodegenerative diseases. Primary Progressive Aphasia (PPA) is a neurodegenerative disease
characterized by a gradual dissolution of language abilities, being these patients regarded with
special attention since they possess higher risk to evolve to dementia. Consequently,
discovering the different subtypes of PPA patients is fundamental to the timely administration

Evaluating differential gene expression using RNA-sequencing data

11/28/2013 - 14:30
11/28/2013 - 15:30

Unlike the genome, the cell transcriptome is dynamic and specific for a given cell developmental stage or physiological condition. Understanding the transcriptome is essential for interpreting the functional elements of the genome and revealing the molecular constituents of cells. Recently, developments of high-throughput DNA sequencing methodologies have provided a new method to sequence RNA at unprecedented high resolutions. This method is termed RNA-Seq and has been emerging as the preferred technology for both characterization and quantification of the cell transcripts.


10/31/2013 - 14:30
10/31/2013 - 15:30

Metagenomics is the study of metagenomes, unprocessed genetic material residing in the most varied
sites, without separation into individual organisms. Metagenomic approaches to the study of biological
communities are quickly changing our understanding of the function and inter-relationships among
living organisms in ecosystems. The rapid advances in metagenomics are largely due to the hasty development
of high throughput platforms for deoxyribonucleic acid (DNA) sequencing, that need to be
accompanied by significant advances in data analysis techniques.

On Multi-class Classification Problems Using Genetic Programming

10/24/2013 - 14:30
10/24/2013 - 15:30

Genetic Programming (GP) is a field under the hood of Evolutionary
Computing, that has been successful in addressing a variety of
problems in the field of data mining and machine learning,
notexcluding the problems of multi-class classification
(mcc). However, its realms have been successful only in extending the
binary GP classifiers to the problems of mcc, thereof still retaining
a void of not having any efficient multi-class classifiers, when
compared to non-GP classifiers. In this work, I will present a novel
algorithm that incorporates some ideas on the representation of the

Quick Hyper-Volume

10/10/2013 - 14:30
10/10/2013 - 15:30

I will present a new algorithm to calculate exact hypervolumes. Given
a set of $d$-dimensional points, it computes the
hypervolume of the dominated space. Determining this value is an
important subroutine of Multiobjective Evolutionary Algorithms
(MOEAs). We analyze the ``Quick Hypervolume'' QHV algorithm
theoretically and experimentally. The theoretical results are
a significant contribution to the current state of the art. Moreover
the experimental performance is also very competitive, compared
with existing exact hypervolume algorithms.

Parallel efficient alignment of reads for re-sequencing applications

09/26/2013 - 14:30
09/26/2013 - 15:30

In bioinformatics, in the context of resequencing projects,
the e cient and accurate mapping of reads to a reference
genome is a critical problem. One instance of this problem
is the local alignment of pyrosequencing reads produced
by the 454 GS FLX system against a reference sequence,
an instance for which the software tool TAPyR (Tool for
the Alignment of Pyrosequencing Reads) was developed.
TAPyR implements a methodology to e ciently solve this
problem, which proved to yield results of a quality (both in
terms of content and execution speed) higher than those of

Identification of Hybrid Time-varying Parameter systems with Particle Filtering and Expectation Maximization

07/19/2012 - 11:00
07/19/2012 - 12:00

Abstract:One limiting assumption of many mathematical models for dynamic systems is that the parameters of the
system do not change during the observation period, which however does
not necessary hold in many cases.
This is typical for biological and medical systems, where we observe a high intra-individual variability in the model parameters. Hybrid time-varying parameter framework is able to capture the changes of parameters that may represent the change of state of the individual, for example

Host-pathogen interaction upon infection with Listeria using NGS techniques

06/07/2013 - 11:00
06/07/2013 - 12:00

Listeria monocytogenes is a model bacterial pathogen whose, after internalization, is
capable of disrupting a double-membrane vacuole, replicate in the host cytosol and
manipulate the innate response triggered in the cytosol. Its intracellular lifecycle in the
human host provides insight into the dynamics of general host-pathogen
interactions. The identification of host sequences affected during these interactions is
paramount to our understanding of how pathogens engineer their cellular

Unravelling communities of ALS patients using network mining

06/21/2012 - 11:00
06/21/2012 - 12:00

Amyotrophic Lateral Sclerosis is a devastating neurodegenerative
disease characterized by a usually fast progression of
muscular denervation, generally leading to death in a few
years from onset. In this context, any significant improvement
of the patient's life expectancy and quality is of major
relevance. Several studies have been made to address problems
such as ALS diagnosis, and more recently, prognosis.
However, these analysis have been mostly restricted to classical
statistical approaches used to find the most associated

Novel semantic approaches in Genetic Programming.

05/24/2013 - 11:00
05/24/2013 - 12:00

Evolutionary algorithms are stochastic optimization techniques based on the
principles of natural evolution and Genetic Programming (GP) belongs to this family .

In recent years the study of GP systems has been extended to phenotypic aspects while in previous phase it was mainly focused on genotypic and syntactic aspects.