Modelling Stem Cells Lineages with Markov Trees
(2009) 4th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2009 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5780 LNBI. p.233-243- Abstract
A variational Bayesian EM with smoothed probabilities algorithm for hidden Markov trees (HMT) is proposed for incomplete tree structured data. The full posterior of the HMT parameters is determined and the underflow problems associated with previous algorithms are eliminated. Example results for the prediction of the types of cells in real stem cell lineage trees are presented.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/f490b0be-cd8c-40b9-b357-e03d9092bc3a
- author
- Olariu, Victor LU ; Coca, Daniel ; Billings, Stephen A. and Kadirkamanathan, Visakan
- publishing date
- 2009-10-16
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- Pattern Recognition in Bioinformatics - 4th IAPR International Conference, PRIB 2009, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- volume
- 5780 LNBI
- pages
- 11 pages
- conference name
- 4th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2009
- conference location
- Sheffield, United Kingdom
- conference dates
- 2009-09-07 - 2009-09-09
- external identifiers
-
- scopus:70349847568
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 3642040306
- 9783642040306
- DOI
- 10.1007/978-3-642-04031-3_21
- language
- English
- LU publication?
- no
- id
- f490b0be-cd8c-40b9-b357-e03d9092bc3a
- date added to LUP
- 2019-05-31 15:47:46
- date last changed
- 2024-10-02 03:05:56
@inproceedings{f490b0be-cd8c-40b9-b357-e03d9092bc3a, abstract = {{<p>A variational Bayesian EM with smoothed probabilities algorithm for hidden Markov trees (HMT) is proposed for incomplete tree structured data. The full posterior of the HMT parameters is determined and the underflow problems associated with previous algorithms are eliminated. Example results for the prediction of the types of cells in real stem cell lineage trees are presented.</p>}}, author = {{Olariu, Victor and Coca, Daniel and Billings, Stephen A. and Kadirkamanathan, Visakan}}, booktitle = {{Pattern Recognition in Bioinformatics - 4th IAPR International Conference, PRIB 2009, Proceedings}}, isbn = {{3642040306}}, issn = {{0302-9743}}, language = {{eng}}, month = {{10}}, pages = {{233--243}}, series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}}, title = {{Modelling Stem Cells Lineages with Markov Trees}}, url = {{http://dx.doi.org/10.1007/978-3-642-04031-3_21}}, doi = {{10.1007/978-3-642-04031-3_21}}, volume = {{5780 LNBI}}, year = {{2009}}, }