[bull-ia] PhD position: Biological sub-graph identification thanks to metaheuristics

PhD Position on « Biological sub-graph identification thanks to
metaheuristics » at the I3S laboratory (http://www.i3s.unice.fr) in

Application : a curiculum vitae should be sent to Claude Pasquier
(Claude.Pasquier@unice.fr) and Denis Pallez (Denis.Pallez@unice.fr)
before 19th May 2019.

According to World Health Organization, cancer is the second leading
cause of death globally, and is responsible for around 10 million deaths
in 2018. The most common one is lung cancer responsible of more than 2
million cases. Two classes of lung cancer exists which grow and spread
differently in patients. Treatment options depend mainly on the type and
stage of the cancer (Lemjabbar-Alaoui et al., 2015). In order to develop
more efficacious and more specific drugs, scientists must better
understand complex biological systems related to this cancer. For this
reason, biological networks – containing functional interactions between
genes, proteins, DNAs, RNAs… – have been created by biologists and
stored in public databases. For instance, graphs with human genes as
nodes and interactions between nodes as edges are considered.
For understanding disease mechanisms, active module (i.e. well-connected
subnetworks that significantly and collaboratively react to certain
conditions) are mined in one or more biological networks at a time. To
each condition is associated a score, a mathematical function. The
problem of finding optimal subnetworks with the highest score is
NP-hard. Main computational methods for solving the active subnetworks
identification problem (Nguyen et al., 2019) are (i) greedy algorithms,
(ii) random walk algorithms, (iii) diffusion emulation models, (iv)
genetic algorithms, (v) maximal clique identification and (vi)
clustering based methods. First two methods are simple and rapid but are
highly dependent to the starting point of the algorithm that does not
guarantee to reach global optima. Conversely, methods (iii) and (iv) are
able to find global optima or an approximation of it at the prize of a
computational burden. Finally, methods (v) and (vi) do not fully answer
to the initial issue as it is not true that all genes of the resulting
clique take part in the biological process or as number of genes in
optimal cluster is too huge for being understandable.

Nevertheless, all previous methods only consider static information
without taking into account the kinetic interactions that exist in
biological systems. That is why we are interested, in this work, to
build a memetic algorithm (Cotta et al., 2018) that combines a global
search technique with a local search technique for active module
identification in a multi-objective and dynamic context. Scalable
benchmarks can be used for testing (Jiang et al., 2019).

Skills and profile:
• Master degree in Computer Science, especially in Optimization and/or
Data Mining
• Programming skills are needed.
• Knowledge in Bioinformatics could be a great help.
• Oral and written english is required. French is not mandatory.

Cotta, C., Mathieson, L., Moscato, P., 2018. Memetic Algorithms, in:
Martí, R., Pardalos, P.M., Resende, M.G.C. (Éd.), Handbook of
Heuristics. Springer International Publishing, Cham, p. 607‑638.
Jiang, S., Kaiser, M., Yang, S., Kollias, S., Krasnogor, N., 2019. A
Scalable Test Suite for Continuous Dynamic Multiobjective Optimization.
IEEE Trans. Cybern. 1‑13. https://doi.org/10.1109/TCYB.2019.2896021
Lemjabbar-Alaoui, H., Hassan, O.U., Yang, Y.-W., Buchanan, P., 2015.
Lung cancer: Biology and treatment options. Biochim. Biophys. Acta 1856,
189‑210. https://doi.org/10.1016/j.bbcan.2015.08.002
Nguyen, H., Shrestha, S., Tran, D., Shafi, A., Draghici, S., Nguyen, T.,
2019. A Comprehensive Survey of Tools and Software for Active Subnetwork
Identification. Front. Genet. 10. https://doi.org/10.3389/fgene.2019.00155

(‘ O-O ‘)
Denis PALLEZ denis.pallez@unice.fr
Maitre de Conférences / Associate Professor
Université Côte d’Azur (IUT), CNRS, I3S
(+33) 4 89 15 42 85

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