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323 results

102. MAP estimation algorithm for phase response curves based on analysis of the observation process.

Author

Ota K, Omori T, and Aonishi T

Subjects
Action Potentials, Bayes Theorem, Likelihood Functions, Models, Neurological, Time Factors, Algorithms, Neurons physiology
Abstract

Many research groups have sought to measure phase response curves (PRCs) from real neurons. However, methods of estimating PRCs from noisy spike-response data have yet to be established. In this paper, we propose a Bayesian approach for estimating PRCs. First, we analytically obtain a likelihood function of the PRC from a detailed model of the observation process formulated as Langevin equations. Then we construct a maximum a posteriori (MAP) estimation algorithm based on the analytically obtained likelihood function. The MAP estimation algorithm derived here is equivalent to the spherical spin model. Moreover, we analytically calculate a marginal likelihood corresponding to the free energy of the spherical spin model, which enables us to estimate the hyper-parameters, i.e., the intensity of the Langevin force and the smoothness of the prior.

Published
2009
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104. Detecting common gene expression patterns in multiple cancer outcome entities.

Author

Yang X, Bentink S, and Spang R

Subjects
Biomarkers, Tumor analysis, Gene Expression Regulation, Neoplastic, Humans, Neoplasm Proteins analysis, Prognosis, Reproducibility of Results, Sensitivity and Specificity, Software, Algorithms, Biomarkers, Tumor metabolism, Diagnosis, Computer-Assisted methods, Gene Expression Profiling methods, Neoplasm Proteins metabolism, Neoplasms diagnosis, Neoplasms metabolism, Pattern Recognition, Automated methods
Abstract

Most oncological microarray studies focus on molecular distinctions in different cancer entities. Recently, researchers started using microarrays for investigating molecular commonalities of multiple cancer types. This poses novel bioinformatics challenges. In this paper we describe a method that detects common molecular mechanisms in different cancer entities. The method extends previously described concepts by introducing Meta-Analysis Pattern Matches. In an analysis of four prognostic cancer studies, involving breast cancer, leukemia, and mesothelioma, we are able to identify 42 genes that show consistent up- or down-regulation in patients with a poor disease outcome. These genes complement the set of previously published candidates for universal prognostic markers in cancer.

Published
2005
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