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  History-alignment Models for Bias-aware Prediction of Virological Response to HIV Combination Therapy

Bogojeska, J., Stöckel, D., Zazzi, M., Kaiser, R., Incardona, F., Rosen-Zvi, M., et al. (2012). History-alignment Models for Bias-aware Prediction of Virological Response to HIV Combination Therapy. In N. Lawrence, & M. Girolami (Eds.), Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012) (pp. 118-126). La Palma, Canary Islands, Spain: Journal of Machine Learning Research.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0014-C576-4 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0023-C0E6-7
Genre: Conference Paper
Latex : History-alignment Models for Bias-aware Prediction of Virological Response to {HIV} Combination Therapy

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2012
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Appearing in Proceedings of the 15 th International Con- ference on Artificial Intelligence and Statistics (AISTATS) 2012, La Palma, Canary Islands. Volume XX of JMLR:W&CP XX. Copyright 2012 by the authors.
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 Creators:
Bogojeska, Jasmina1, Author           
Stöckel, Daniel2, Author
Zazzi, Maurizio2, Author
Kaiser, Rolf2, Author
Incardona, Francesca2, Author
Rosen-Zvi, Michal2, Author
Lengauer, Thomas1, Author           
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
2External Organizations, ou_persistent22              

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 Abstract: The relevant HIV data sets used for predicting outcomes of HIV combination therapies suffer from several problems: different treatment backgrounds of the samples, uneven representation with respect to the level of therapy experience and uneven therapy representation. Also, they comprise only viral strain(s) that can be detected in the patients� blood serum. The approach presented in this paper tackles these issues by considering not only the most recent therapies but also the different treatment backgrounds of the samples making up the clinical data sets when predicting the outcomes of HIV therapies. For this purpose, we introduce a similarity measure for sequences of therapies and use it for training separate linear models for predicting therapy outcome for each target sample. Compared to the most commonly used approach that encodes all available treatment information only by specific input features our approach has the advantage of delivering significantly more accurate predictions for therapy-experienced patients and for rare therapies. Additionally, the sample-specific models are more interpretable which is very important in medical applications.

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Language(s): eng - English
 Dates: 2012-04
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Bogojeska2012b
Other: Local-ID: 124D02AE50A81550C1257AD20046A1AC-Bogojeska2012b
 Degree: -

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Title: Fifteenth International Conference on Artificial Intelligence and Statistics
Place of Event: La Palma, Canary Islands, Spain
Start-/End Date: 2012-04-21 - 2012-04-23

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Title: Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012)
  Abbreviation : AISTATS 2012
Source Genre: Proceedings
 Creator(s):
Lawrence, Neil1, Editor
Girolami, Mark1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: La Palma, Canary Islands, Spain : Journal of Machine Learning Research
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 118 - 126 Identifier: -

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 22 Sequence Number: - Start / End Page: - Identifier: -