| DOI | Trouver le DOI : https://doi.org/10.1016/j.neucom.2018.02.096 |
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| Auteur | Rechercher : Li, Yifeng1; Rechercher : Fauteux, François1; Rechercher : Zou, Jinfeng2; Rechercher : Nantel, André2; Rechercher : Pan, Youlian1 |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
- Conseil national de recherches Canada. Thérapeutique en santé humaine
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| Format | Texte, Article |
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| Sujet | cancer diagnosis; whole exome sequencing; deep learning; multi-modal deep Boltzmann machine |
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| Résumé | When diagnosed at an advanced stage, most cancer patients suffer from treatment failure, recurrences and low survival. Taking advantage of high-throughput sequencing and deep learning techniques, we developed an early cancer monitoring method based on multi-modal deep Boltzmann machine to (1) learn association between matched germline and somatic mutations captured by whole exome sequencing from available samples of cancer patients, and (2) predict patient-specific high-risk genes whose somatic mutations are required to drive normal tissues to a tumor state. Our experiments on a set of breast cancer samples show that our method significantly outperforms the currently used frequency-based method in the personalized prediction of genes carrying critical mutations. |
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| Date de publication | 2018-05-22 |
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| Maison d’édition | Elsevier |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 23003839 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 28a1b558-61f2-4be0-b15c-043854aacf54 |
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| Enregistrement créé | 2018-08-14 |
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| Enregistrement modifié | 2020-03-16 |
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