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Network-guided feature selection in high-dimensional genomic data
Post-edited
Authors : Azencott, Chloé (Author of the conference)
CIRM (Publisher )
62P10 - Applications of statistics to biology and medical sciences
92-08 - Computational methods
92B15 - General biostatistics, See also {62P10}
92C42 - Systems biology, networks
Additional resources :
https://github.com/chagaz
https://github.com/mahito-sugiyama/Multi-SConES
CIRM (Publisher )
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| GWAS statistical test selection stability network-guided GWAS regularization network regularization multitask |
Abstract :
Differences in disease predisposition or response to treatment can be explained in great part by genomic differences between individuals. This has given birth to precision medicine, where treatment is tailored to the genome of patients. This field depends on collecting considerable amounts of molecular data for large numbers of individuals, which is being enabled by thriving developments in genome sequencing and other high-throughput experimental technologies.
Unfortunately, we still lack effective methods to reliably detect, from this data, which of the genomic features determine a phenotype such as disease predisposition or response to treatment. One of the major issues is that the number of features that can be measured is large (easily reaching tens of millions) with respect to the number of samples for which they can be collected (more usually of the order of hundreds or thousands), posing both computational and statistical difficulties.
In my talk I will discuss how to use biological networks, which allow us to understand mutations in their genomic context, to address these issues. All the methods I will present share the common hypotheses that genomic regions that are involved in a given phenotype are more likely to be connected on a given biological network than not.
Keywords : bioinformatique; data integration
MSC Codes : Unfortunately, we still lack effective methods to reliably detect, from this data, which of the genomic features determine a phenotype such as disease predisposition or response to treatment. One of the major issues is that the number of features that can be measured is large (easily reaching tens of millions) with respect to the number of samples for which they can be collected (more usually of the order of hundreds or thousands), posing both computational and statistical difficulties.
In my talk I will discuss how to use biological networks, which allow us to understand mutations in their genomic context, to address these issues. All the methods I will present share the common hypotheses that genomic regions that are involved in a given phenotype are more likely to be connected on a given biological network than not.
Keywords : bioinformatique; data integration
62P10 - Applications of statistics to biology and medical sciences
92-08 - Computational methods
92B15 - General biostatistics, See also {62P10}
92C42 - Systems biology, networks
Additional resources :
https://github.com/chagaz
https://github.com/mahito-sugiyama/Multi-SConES
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 23/03/2020 Conference Date : 05/03/2020 Subseries : Research talks arXiv category : Machine Learning ; Quantitative Biology Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:27:51 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2020-03-05_Azencott.mp4 
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Information on the Event
Event Title : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaireEvent Organizers : Baudot, Anais ; Hubert, Florence ; Mossé, Brigitte ; Rémy, Elisabeth ; Tichit, Laurent ; Vignes, Matthieu Dates : 02/03/2020 - 06/03/2020 Event Year : 2020 Event URL : https://conferences.cirm-math.fr/2305.html
Citation Data
DOI : 10.24350/CIRM.V.19620603Cite this video as: Azencott, Chloé (2020). Network-guided feature selection in high-dimensional genomic data. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19620603 URI : http://dx.doi.org/10.24350/CIRM.V.19620603 |
See Also
- [Multi angle] Part 2 - Dynamic logic models complement machine learning for personalized medicine / Author of the conference Saez-Rodriguez, Julio.
- [Multi angle] Matrix factorisation techniques for data integration / Author of the conference Lê Cao, Kim-Anh.
- [Multi angle] Cell processes in space and time - exemplified with yeast mating / Author of the conference Klipp, Edda.
- [Multi angle] Network approaches for personalized medicine / Author of the conference Martinez-Rodriguez, Maria.
- [Multi angle] Learning interpretable networks from multivariate information in biological and clinical data / Author of the conference Isambert, Hervé.
- [Multi angle] From data to biological processes: interactions, networks and analyses, to understand cellular functions / Author of the conference Brun, Christine.
- [Multi angle] Part 1 - Networks of prior knowledge as frames to understand complex biological data / Author of the conference Saez-Rodriguez, Julio.
Bibliography
- AZENCOTT, Chloé-Agathe, GRIMM, Dominik, SUGIYAMA, Mahito, et al. Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 2013, vol. 29, no 13, p. i171-i179. - https://doi.org/10.1093/bioinformatics/btt238
- SUGIYAMA, Mahito, AZENCOTT, Chloé-Agathe, GRIMM, Dominik, et al. Multi-task feature selection on multiple networks via maximum flows. In : Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014. p. 199-207. - https://doi.org/10.1137/1.9781611973440.23
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