BACKGROUND: Boolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors-subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks. RESULTS: In this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method's applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus. CONCLUSIONS: The proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system's stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings.

• MeSH
• Algorithms MeSH
• Aniline Compounds MeSH
• Benzamides MeSH
• COVID-19 * MeSH
• Gene Regulatory Networks * MeSH
• Humans MeSH
• Models, Genetic MeSH
• Naphthalenes MeSH
• SARS-CoV-2 MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

It has been known for discrete-time recurrent neural networks (NNs) that binary-state models using the Heaviside activation function (with Boolean outputs 0 or 1) are equivalent to finite automata (level 3 in the Chomsky hierarchy), while analog-state NNs with rational weights, employing the saturated-linear function (with real-number outputs in the interval [0,1]), are Turing complete (Chomsky level 0) even for three analog units. However, it is as yet unknown whether there exist subrecursive (i.e. sub-Turing) NN models which occur on Chomsky levels 1 or 2. In this paper, we provide such a model which is a binary-state NN extended with one extra analog unit (1ANN). We achieve a syntactic characterization of languages that are accepted online by 1ANNs in terms of so-called cut languages which are combined in a certain way by usual operations. We employ this characterization for proving that languages accepted by 1ANNs with rational weights are context-sensitive (Chomsky level 1) and we present explicit examples of such languages that are not context-free (i.e. are above Chomsky level 2). In addition, we formulate a sufficient condition when a 1ANN recognizes a regular language (Chomsky level 3) in terms of quasi-periodicity of parameters derived from its real weights, which is satisfied e.g. for rational weights provided that the inverse of the real self-loop weight of the analog unit is a Pisot number.

• MeSH
• Language * MeSH
• Neural Networks, Computer * MeSH
• Models, Theoretical * MeSH
• Publication type
• Journal Article MeSH

We propose a novel hybrid single-electron device for reprogrammable low-power logic operations, the magnetic single-electron transistor (MSET). The device consists of an aluminium single-electron transistor with a GaMnAs magnetic back-gate. Changing between different logic gate functions is realized by reorienting the magnetic moments of the magnetic layer, which induces a voltage shift on the Coulomb blockade oscillations of the MSET. We show that we can arbitrarily reprogram the function of the device from an n-type SET for in-plane magnetization of the GaMnAs layer to p-type SET for out-of-plane magnetization orientation. Moreover, we demonstrate a set of reprogrammable Boolean gates and its logical complement at the single device level. Finally, we propose two sets of reconfigurable binary gates using combinations of two MSETs in a pull-down network.

• MeSH
• Transistors, Electronic * MeSH
• Magnetic Fields * MeSH
• Models, Theoretical * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Supervised learning of perceptron networks is investigated as an optimization problem. It is shown that both the theoretical and the empirical error functionals achieve minima over sets of functions computable by networks with a given number n of perceptrons. Upper bounds on rates of convergence of these minima with n increasing are derived. The bounds depend on a certain regularity of training data expressed in terms of variational norms of functions interpolating the data (in the case of the empirical error) and the regression function (in the case of the expected error). Dependence of this type of regularity on dimensionality and on magnitudes of partial derivatives is investigated. Conditions on the data, which guarantee that a good approximation of global minima of error functionals can be achieved using networks with a limited complexity, are derived. The conditions are in terms of oscillatory behavior of the data measured by the product of a function of the number of variables d, which is decreasing exponentially fast, and the maximum of the magnitudes of the squares of the L(1)-norms of the iterated partial derivatives of the order d of the regression function or some function, which interpolates the sample of the data. The results are illustrated by examples of data with small and high regularity constructed using Boolean functions and the gaussian function.

• MeSH
• Algorithms MeSH
• Biological Clocks physiology   MeSH
• Financing, Organized MeSH
• Logic MeSH
• Neural Networks, Computer MeSH
• Normal Distribution MeSH
• Computer Simulation MeSH
• Artificial Intelligence MeSH

This article addresses the topic of extracting logical rules from data by means of artificial neural networks. The approach based on piecewise linear neural networks is revisited, which has already been used for the extraction of Boolean rules in the past, and it is shown that this approach can be important also for the extraction of fuzzy rules. Two important theoretical properties of piecewise-linear neural networks are proved, allowing an elaboration of the basic ideas of the approach into several variants of an algorithm for the extraction of Boolean rules. That algorithm has already been used in two real-world applications. Finally, a connection to the extraction of rules of the Łukasiewicz logic is established, relying on recent results about rational McNaughton functions. Based on one of the constructive proofs of the McNaughton theorem, an algorithm is formulated that in principle allows extracting a particular kind of formulas of the Łukasiewicz predicate logic from piecewise-linear neural networks trained with rational data.

• MeSH
• Algorithms MeSH
• Ecology MeSH
• Financing, Organized MeSH
• Fuzzy Logic MeSH
• Data Interpretation, Statistical MeSH
• Humans MeSH
• Linear Models MeSH
• Neural Networks, Computer MeSH
• Pattern Recognition, Automated methods   MeSH
• Artificial Intelligence MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Comparative Study MeSH

• Keywords
• technika výpočetní, ORACLE9i,
• Publication type
• Handbook MeSH

Tato kniha vysvětlí všem webovým vývojářům i ostatním programátorům základy XSLT, vytváření a používání stylů XSL a šablon, porovnávacích vzorů, modifikaci obsahu dokumentů, princip a praktické využití jazyka XPath, funkce XSLT, pojmenované šablony, parametry, práci s proměnnými či s rozhraními API procesorů XSLT i formátování textů, tabulek, seznamů, obrázků, odkazů, sloupců, záhlaví a dalších obsahových prvků pomocí XSLT.

• MeSH
• Internet MeSH
• Computer Communication Networks MeSH

• Conspectus
• Programování. Software

• MeSH
• Computer Communication Networks MeSH
• Computer Systems MeSH

• Conspectus
• Programování. Software

• Keywords
• Biologie, Evoluce, Fylogeneze,
• MeSH
• Biological Evolution MeSH
• Biology MeSH
• Phylogeny MeSH
• Evolution, Molecular MeSH
• Origin of Life MeSH

• Conspectus
• Obecná genetika. Obecná cytogenetika. Evoluce
• NML Fields
• molekulární biologie, molekulární medicína