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Based on statistical thermodynamics principle or Michaelis-Menten kinetics equation, the models for biological systems contain linear fractional functions as reaction rates which are nonlinear in both parameters and states. Generally it is challenging to estimate parameters nonlinear in a model although there have been many traditional nonlinear parameter estimation methods such as Gauss-Newton iteration...
Based on statistical thermodynamics or Michaelis-Menten kinetics, molecular biological systems can be modeled by a system of nonlinear differential equations. The nonlinearity in the model stems from rational reaction rates whose numerator and denominator are linear in parameters. It is a nonlinear problem to estimate the parameters in such rational models of molecular biological systems. In principle,...
Several computational methods have been proposed to assess the quality of tandem mass spectra. These methods range from supervised to unsupervised algorithms, discriminative to generative models. Unsupervised learning algorithms for tandem mass spectra are not probabilistic model based and they don't provide probabilities for spectra quality assessment. In this study, the distribution of high quality...
Machine learning algorithms are widely used for quality assessment of tandem mass spectra based on a number of features. However, it is still unclear which features are most relevant to the quality of tandem mass spectra. In this paper, a sparse logistical regression method is proposed for selecting the most relevant features from those features found in the literature. To investigate the performance...
Models for biological systems derived from the generalized mass action law are typically a group of nonlinear ordinary differential equations. However, parameters in such models can be separated into two groups: one group of parameters linear in the model and another group of parameters nonlinear in the model. This paper introduces a separable parameter estimation method to estimate the parameters...
In this paper, we present a novel method to mine, model, and evaluate a regulatory system executing cellular functions that can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to such a biomolecular network to obtain various subnetworks. Second, computational models are generated for the subnetworks and simulated...
It is well known that a majority of experimental spectra are of too poor quality to be interpreted by any automatic method. It wastes time to interpret these "un-interpretable" spectra. On the other hand, some spectra with high quality also cannot be interpreted by any automatic method, but maybe by manual checking. Therefore, it is worthwhile to develop a powerful filter that could eliminate...
The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It is sensitive to initial partitions, its result is prone to the local minima, and it is only applicable to data with spherical-shape clusters. The last shortcoming means that we must assume that gene expression data at the different...
DNA microarray technologies allow for the simultaneous monitoring of thousands of genes, which reveal important information about cellular and tissue expression phenotypes. From a viewpoint of data analysis, microarray experiments may be classified into (1) classification of patients or non-patients or more subtypes in terms of gene expressions, (2) discovery of gene expression patterns over a set...
Tandem mass spectrometry has emerged as a cornerstone of high throughput proteomic studies owing in part to various high throughput search engines which are used to interpret these tandem mass spectra. However, majority of experimental tandem mass spectra cannot be interpreted by any existing search engines or other methods. There are many reasons why this happens. However, one of the most important...
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