Invited Session Thu.1.MA 376

Thursday, 10:30 - 12:00 h, Room: MA 376

Cluster 12: Life sciences & healthcare [...]

Life sciences and healthcare "à la Clermontoise"

 

Chair: Annegret K. Wagler

 

 

Thursday, 10:30 - 10:55 h, Room: MA 376, Talk 1

Vincent Barra
Assessing functional brain connectivity changes in cognitive aging using RS-fMRI and graph theory

Coauthors: Clément de Ribet, Rune Eikeland, Erik Hanson, Erlend Hodneland, Arvid Lundervold, Tessa Welte

 

Abstract:
The observation that spontaneous BOLD fMRI activity is not random noise, but is organized in the resting human brain as functionally relevant resting state networks has generated a new avenue in neuroimaging and cognitive research, where brain connectivity and graph theory are increasingly important concepts for understanding and for computation. We investigate functional brain connectivity and graph analysis methodology applied to the aging brain at two quite different time scales. The study involves whole brain BOLD fMRI measurements, conducted at time t1 and t2 3 years later, designing binary functional connectivity graphs Gi1 and Gi2 for subjects i=1,N. We computed local and global nodal network metrics to assess functional connectivity changes between these graph collections. We found individual and group-wise reduction from t1 to t2 in all local and global graph indices. These findings were uniform across different threshold values used for thresholding the Pearson's correlations (edge weights) in order to obtain the binary graphs. Several perspectives are proposed by these preliminary results, eg in the context of test-retest reliability and reproducibility of graph metric.

 

 

Thursday, 11:00 - 11:25 h, Room: MA 376, Talk 2

Engelbert Mephu Nguifo
Stability measurement of motif extraction methods from protein sequences in classification tasks

Coauthors: Sabeur Aridhi, Mondher Maddouri, Rabie Saidi

 

Abstract:
Feature extraction is an unavoidable task, especially in the critical
step of pre-processing of biological sequences. This step consists for example in
transforming the biological sequences into vectors of motifs where each motif
is a subsequence that can be seen as a property (or attribute) characterizing the
sequence. Hence, we obtain an object-property table where objects are
sequences and properties are motifs extracted from
sequences. This table can be used to apply standard machine learning tools to
perform data mining tasks such as classification. Previous works described
motif extraction methods for sequences classification, but none of them
discussed the robustness of these methods when perturbing the input data. In
this work, we introduce the notion of stability of the generated motifs in order
to study the robustness of motif extraction methods. We express this robustness
in terms of the ability of the method to reveal any change occurring in the input
data and also its ability to target the interesting motifs. We use these criteria to
evaluate and experimentally compare four existing methods.

 

 

Thursday, 11:30 - 11:55 h, Room: MA 376, Talk 3

Romain Pogorelcnik
Clique separator decomposition and applications to biological data

Coauthors: Anne Berry, Annegret Wagler

 

Abstract:
The study of gene interactions is an important research area in biology. Nowadays, high-throughput techniques are available to obtain gene expression data, and clustering is a first mandatory step towards a better understanding of the functional relationships between genes. We propose a new approach using graphs to model this data, and decompose the graphs by means of clique minimal separators.
A clique separator is a clique whose removal increases the number of connected components of the graph; the decomposition is obtained by repeatedly copying a clique separator into the components it defines, until only subgraphs with no clique separators are left: these subgraphs will be our clusters.
The advantage of our approach is that this decomposition can be computed efficiently, is unique, and yields overlapping clusters. The latter enables us to visualize the data by a meta-graph where two clusters are adjacent if they intersect.
In addition, clique separators help to identify special genes, called fusion genes, in sequence similarity networks, in the context of evolutionary history.
Our first results applying this approach to transcriptomic data are promising.

 

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