University of Cambridge > Talks.cam > Computational Neuroscience > motifNet: Deep learning for system identification of regulatory networks

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

motifNet: Deep learning for system identification of regulatory networks

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Daniel McNamee.

System identification of gene regulatory networks based on proteomics or transcriptomics time course data remains a hard problem, where even state-of-the-art algorithms perform relatively poorly in the regime of large system sizes. This work investigates whether machine learning methods, particularly deep learning, can exploit features of biological networks beyond first principles of chemical kinetics in order to improve performance on this task. We devised a deep neural network architecture called motifNet, combining convolutional and recurrent neural network elements, to tackle this problem. Our framework performs better than the state-of-the-art comparison methods in terms of area under the ROC and PR curve on unseen time course data simulated from the S. cerevisiae and E. coli gene regulatory network.

This talk is part of the Computational Neuroscience series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

International III Pint of Knowledge DAMTP Friday GR Seminar Rausing Lecture Talks related to sustainability and the environment Adaptation to climate change seminar series

Other talks

The race to solve the solar metallicity problem with neutrinos and discover dark matter Attentional episodes and cognitive control Effective Conference Presentations and Networking TODAY Adrian Seminar: "Starting new actions and learning from it" Biological and Clinical Features of High Grade Serous Ovarian Cancer CGHR Practitioner Series: Sharath Srinivasan, Africa's Voices
 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity