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A global structural...
A global structural em algorithm for a model of cancer progression
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- Tofigh, Ali (författare)
- KTH,Beräkningsbiologi, CB,School of Computer Science, McGill Centre for Bioinformatics, McGill University, Canada
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- Sjölund, Erik (författare)
- KTH,Beräkningsbiologi, CB,Stockholm Bioinformatics Center, Stockholm University, Sweden
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- Höglund, Mattias (författare)
- Department of Oncology, Lund University, Sweden
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- Lagergren, Jens (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab,SeRC - Swedish e-Science Research Centre,Beräkningsbiologi, CB
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(creator_code:org_t)
- Neural Information Processing Systems, 2011
- 2011
- Engelska.
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Ingår i: Adv. Neural Inf. Process. Syst.: Annu. Conf. Neural Inf. Process. Syst., NIPS. - : Neural Information Processing Systems.
- Relaterad länk:
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https://kth.diva-por... (primary) (Raw object)
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Abstract
Ämnesord
Stäng
- Cancer has complex patterns of progression that include converging as well as diverging progressional pathways. Vogelstein's path model of colon cancer was a pioneering contribution to cancer research. Since then, several attempts have been made at obtaining mathematical models of cancer progression, devising learning algorithms, and applying these to cross-sectional data. Beerenwinkel et al. provided, what they coined, EM-like algorithms for Oncogenetic Trees (OTs) and mixtures of such. Given the small size of current and future data sets, it is important to minimize the number of parameters of a model. For this reason, we too focus on tree-based models and introduce Hidden-variable Oncogenetic Trees (HOTs). In contrast to OTs, HOTs allow for errors in the data and thereby provide more realistic modeling. We also design global structural EM algorithms for learning HOTs and mixtures of HOTs (HOT-mixtures). The algorithms are global in the sense that, during the M-step, they find a structure that yields a global maximum of the expected complete log-likelihood rather than merely one that improves it. The algorithm for single HOTs performs very well on reasonable-sized data sets, while that for HOT-mixtures requires data sets of sizes obtainable only with tomorrow's more cost-efficient technologies.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Nyckelord
- Algorithms
- Diseases
- Forestry
- Mathematical Models
- Mixtures
- Learning algorithms
- 'current
- Cancer progression
- Cancer research
- Colon cancer
- Complex pattern
- Data set
- EM algorithms
- Hidden variable
- Path models
- Tree-based model
- Bioinformatics
- Computer science
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)