A Reaction Network Scheme Which Implements Inference and Learning for Hidden Markov Models
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
With a view towards molecular communication systems and molecular multi-agent systems, we propose the Chemical Baum-Welch Algorithm, a novel reaction network scheme that learns parameters for Hidden Markov Models (HMMs). Each reaction in our scheme changes only one molecule of one species to one molecule of another. The reverse change is also accessible but via a different set of enzymes, in a design reminiscent of futile cycles in biochemical pathways. We show that every fixed point of the Baum-Welch algorithm for HMMs is a fixed point of our reaction network scheme, and every positive fixed point of our scheme is a fixed point of the Baum-Welch algorithm. We prove that the “Expectation� step and the “Maximization� step of our reaction network separately converge exponentially fast. We simulate mass-action kinetics for our network on an example sequence, and show that it learns the same parameters for the HMM as the Baum-Welch algorithm.
Originalsprog | Engelsk |
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Titel | DNA Computing and Molecular Programming - 25th International Conference, DNA 25, Proceedings |
Redaktører | Yan Liu, Chris Thachuk |
Antal sider | 26 |
Forlag | Springer |
Publikationsdato | 2019 |
Sider | 54-79 |
ISBN (Trykt) | 9783030268060 |
ISBN (Elektronisk) | 9783030268077 |
DOI | |
Status | Udgivet - 2019 |
Begivenhed | 25th International Conference on DNA Computing and Molecular Programming, DNA 2019 - Seattle, USA Varighed: 5 aug. 2019 → 9 aug. 2019 |
Konference
Konference | 25th International Conference on DNA Computing and Molecular Programming, DNA 2019 |
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Land | USA |
By | Seattle |
Periode | 05/08/2019 → 09/08/2019 |
Sponsor | International Society of Nanoscale Science, Computing, and Engineering, Microsoft Research (USA), National Science Foundation (USA), University of Washington |
Navn | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Vol/bind | 11648 LNCS |
ISSN | 0302-9743 |
Links
- http://arxiv.org/pdf/1906.09410
Accepteret manuskript
ID: 226950914