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- Andersson, Claes R., et al.
(författare)
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Bayesian detection of periodic mRNA time profiles withouth use of training examples
- 2006
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 7, s. 63-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time. RESULTS: A learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented. CONCLUSION: In comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.
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- Andersson, Claes R., et al.
(författare)
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In vitro drug sensitivity-gene expression correlations involve a tissue of origin dependency
- 2007
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Ingår i: Journal of chemical information and modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 47:1, s. 239-248
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Tidskriftsartikel (refereegranskat)abstract
- A major concern of chemogenomics is to associate drug activity with biological variables. Several reports have clustered cell line drug activity profiles as well as drug activity-gene expression correlation profiles and noted that the resulting groupings differ but still reflect mechanism of action. The present paper shows that these discrepancies can be viewed as a weighting of drug-drug distances, the weights depending on which cell lines the two drugs differ in.
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- Andersson, Claes R., et al.
(författare)
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Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors
- 2007
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Ingår i: BMC Systems Biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 1, s. 45-
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Tidskriftsartikel (refereegranskat)abstract
- Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process. Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach. Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.
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- Fryknäs, Mårten, et al.
(författare)
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Phenotype-based screening of mechanistically annotated compounds in combination with gene expression and pathway analysis identifies candidate drug targets in a human squamous carcinoma cell model
- 2006
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Ingår i: Journal of Biomolecular Screening. - : Elsevier BV. - 1087-0571 .- 1552-454X. ; 11:5, s. 457-468
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Tidskriftsartikel (refereegranskat)abstract
- The squamous cell carcinoma HeLa cell line and an epithelial cell line hTERT-RPE with a nonmalignant phenotype were interrogated for HeLa cell selectivity in response to 1267 annotated compounds representing 56 pharmacological classes. Selective cytotoxic activity was observed for 14 of these compounds dominated by cyclic adenosine monophosphate (cAMP) selective phosphodiesterase (PDE) inhibitors, which tended to span a representation of the chemical descriptor space of the library. The PDE inhibitors induced delayed cell death with features compatible with classical apoptosis. The PDE inhibitors were largely inactive when tested against a cell line panel consisting of hematological and nonsquamous epithelial phenotypes. In a genome-wide DNA microarray analysis, PDE3A and PDE2A were found to be significantly increased in HeLa cells compared to the other cell lines. The pathway analysis software PathwayAssist was subsequently used to extract a list of proteins and small molecules retrieved from Medline abstracts associated with the hit compounds. The resulting list consisted of major parts of the cAMP-protein kinase A pathway linking to ERK, P38, and AKT. This molecular network may provide a basis for further exploitation of novel candidate targets for the treatment of squamous cell carcinoma.
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| 7. |
- Gustafsson, Mats G., et al.
(författare)
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Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors
- 2010
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 0933-3657 .- 1873-2860. ; 49:2, s. 93-104
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Tidskriftsartikel (refereegranskat)abstract
- Objective:Successful use of classifiers that learn to make decisions from a set of patient examples require robust methods for performance estimation. Recently many promising approaches for determination of an upper bound for the error rate of a single classifier have been reported but the Bayesian credibility interval (Cl) obtained from a conventional holdout test still delivers one of the tightest bounds. The conventional Bayesian CI becomes unacceptably large in real world applications where the test set sizes are less than a few hundred. The source of this problem is that fact that the Cl is determined exclusively by the result on the test examples. In other words, there is no information at all provided by the uniform prior density distribution employed which reflects complete lack of prior knowledge about the unknown error rate. Therefore, the aim of the study reported here was to study a maximum entropy (ME) based approach to improved prior knowledge and Bayesian CIs, demonstrating its relevance for biomedical research and clinical practice.Method and material:It is demonstrated how a refined non-uniform prior density distribution can be obtained by means of the ME principle using empirical results from a few designs and tests using non-overlapping sets of examples.Results:Experimental results show that ME based priors improve the CIs when employed to four quite different simulated and two real world data sets.Conclusions:An empirically derived ME prior seems promising for improving the Bayesian Cl for the unknown error rate of a designed classifier.
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- Isaksson, Anders, et al.
(författare)
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Cross-validation and bootstrapping are unreliable in small sample classification
- 2008
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Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655 .- 1872-7344. ; 29:14, s. 1960-1965
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Tidskriftsartikel (refereegranskat)abstract
- The interest in statistical classification for critical applications such as diagnoses of patient samples based on supervised learning is rapidly growing. To gain acceptance in applications where the subsequent decisions have serious consequences, e.g. choice of cancer therapy, any such decision support system must come with a reliable performance estimate. Tailored for small sample problems, cross-validation (CV) and bootstrapping (BTS) have been the most commonly used methods to determine such estimates in virtually all branches of science for the last 20 years. Here, we address the often overlooked fact that the uncertainty in a point estimate obtained with CV and BTS is unknown and quite large for small sample classification problems encountered in biomedical applications and elsewhere. To avoid this fundamental problem of employing CV and BTS, until improved alternatives have been established, we suggest that the final classification performance always should be reported in the form of a Bayesian confidence interval obtained from a simple holdout test or using some other method that yields conservative measures of the uncertainty.
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