The article presents a method of protein matrix-assisted laser desorption and ionization-time of flight (TOF) spectra analysis. The method performs peaks detection. Spectra are analysed with Gaussian mixture decomposition. The results obtained are used for peaks identification purposes. The concept of the method is that a single peak is represented by one Gaussian distribution. The expectation-maximization algorithm and maximum likelihood rule are used for spectra processing. The analysis can be done for a set of spectra with use of the mean spectrum, or it may be performed for a single spectrum at a time. The number of mixture-model components is estimated by the Bayesian information criterion. Before the main analysis, a few pre-processing steps need to be done. Spectra should be subjected to calibration, normalization, denoising, baseline correction, etc. The aim of the work is to identify peptides in the analysed sample on the basis of the parameters of the mixture model and to find differences between spectra in the analysed set.

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Global Collaborative Knowledge Systems (GCKS) are based on world wide collaboration in know ledge acquisition, content creation with permanent and fast feedback, and more or less democracy of participants. Knowledge acquisition in GCKS is massive and decentralised due to the big scale of the Internet and special features of software tools. The functionality model of GCKS is presented in this paper.

Optimisation of distribution parameters is a very common problem. There are many sorts of distributions which can be used to model environment processes, biological functions or graphical data. However, it is common that parameters of those distribution may be, partially or completely unknown. Mixture models composed of a few distributions are easier to solve. In such a case simple estimation methods may be used to obtain results. Usually models are composed of several distributions. Those distributions may be of the same or different type. Such models are called mixture models. Finding their parameters may be complicated. Usually in such cases iterative methods need to be used. The paper gives a brief survey of algorithms designed for solving mixtures of distributions and problems connected with their usage.One of the most common method used to obtain mixture model parameters is Expectation-Maximization (EM) algorithm. EM is the iterative algorithm performing maximum likelihood estimation. The authors present the results of adjusting the Gaussian mixture models to the data. It is done with the usage of EM algorithm. The article gives advantages and disadvantages of EM algorithm. Improvements of EM applied in the case of large data are also presented. They help increase efficiency and decrease operation time of the algorithm.Another considered issue is the problem of optimal input parameters selection and its influence on the adjustment results. The authors also present algorithm performance observations. *

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