Symbolic algorithm Dotaz Zobrazit nápovědu
Boolean networks (BNs) are a well-accepted modelling formalism in computational systems biology. Nevertheless, modellers often cannot identify only a single BN that matches the biological reality. The typical reasons for this is insufficient knowledge or a lack of experimental data. Formally, this uncertainty can be expressed using partially specified Boolean networks (PSBNs), which encode the wide range of network candidates into a single structure. In this paper, we target the control of PSBNs. The goal of BN control is to find perturbations which guarantee stabilisation of the system in the desired state. Specifically, we consider variable perturbations (gene knock-out and over-expression) with three types of application time-window: one-step, temporary, and permanent. While the control of fully specified BNs is a thoroughly explored topic, control of PSBNs introduces additional challenges that we address in this paper. In particular, the unspecified components of the model cause a significant amount of additional state space explosion. To address this issue, we propose a fully symbolic methodology that can represent the numerous system variants in a compact form. In fully specified models, the efficiency of a perturbation is characterised by the count of perturbed variables (the perturbation size). However, in the case of a PSBN, a perturbation might work only for a subset of concrete BN models. To that end, we introduce and quantify perturbation robustness. This metric characterises how efficient the given perturbation is with respect to the model uncertainty. Finally, we evaluate the novel control methods using non-trivial real-world PSBN models. We inspect the method's scalability and efficiency with respect to the size of the state space and the number of unspecified components. We also compare the robustness metrics for all three perturbation types. Our experiments support the hypothesis that one-step perturbations are significantly less robust than temporary and permanent ones.
- Klíčová slova
- Boolean network, Permanent control, Perturbation, Symbolic algorithm, Temporary control,
- MeSH
- algoritmy MeSH
- genové regulační sítě * MeSH
- systémová biologie * MeSH
- Publikační typ
- časopisecké články MeSH
In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.
- Klíčová slova
- Cartesian genetic programming, coevolutionary algorithms, evolutionary design, fitness prediction, image processing., symbolic regression,
- MeSH
- algoritmy * MeSH
- biologická evoluce * MeSH
- genetická zdatnost MeSH
- lidé MeSH
- počítačová simulace MeSH
- počítačové zpracování obrazu metody MeSH
- poměr signál - šum MeSH
- regresní analýza MeSH
- software * MeSH
- vylepšení obrazu metody MeSH
- výpočetní biologie metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
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.
- Klíčová slova
- Attractor bifurcation, Boolean networks, Software tool, Symbolic computation, type-1 interferons,
- MeSH
- algoritmy MeSH
- aniliny MeSH
- benzamidy MeSH
- COVID-19 * MeSH
- genové regulační sítě * MeSH
- lidé MeSH
- modely genetické MeSH
- naftaleny MeSH
- SARS-CoV-2 MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- 5-amino-2-methyl-N-((R)-1-(1-naphthyl)ethyl)benzamide MeSH Prohlížeč
- aniliny MeSH
- benzamidy MeSH
- naftaleny MeSH
This article provides an innovative approach for verification by model checking of programs that undergo continuous changes. To tackle the problem of repeating the entire model checking for each new version of the program, our approach verifies programs incrementally. It reuses computational history of the previous program version, namely function summaries. In particular, the summaries are over-approximations of the bounded program behaviors. Whenever reusing of summaries is not possible straight away, our algorithm repairs the summaries to maximize the chance of reusability of them for subsequent runs. We base our approach on satisfiability modulo theories (SMT) to take full advantage of lightweight modeling approach and at the same time the ability to provide concise function summarization. Our approach leverages pre-computed function summaries in SMT to localize the checks of changed functions. Furthermore, to exploit the trade-off between precision and performance, our approach relies on the use of an SMT solver, not only for underlying reasoning, but also for program modeling and the adjustment of its precision. On the benchmark suite of primarily Linux device drivers versions, we demonstrate that our algorithm achieves an order of magnitude speedup compared to prior approaches.
- Klíčová slova
- Craig interpolation, Incremental verification, Program changes, SMT solving, Symbolic model checking,
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Identification of non-trivial and meaningful patterns in omics data is one of the most important biological tasks. The patterns help to better understand biological systems and interpret experimental outcomes. A well-established method serving to explain such biological data is Gene Set Enrichment Analysis. However, this type of analysis is restricted to a specific type of evaluation. Abstracting from details, the analyst provides a sorted list of genes and ontological annotations of the individual genes; the method outputs a subset of ontological terms enriched in the gene list. Here, in contrary to enrichment analysis, we introduce a new tool/framework that allows for the induction of more complex patterns of 2-dimensional binary omics data. This extension allows to discover and describe semantically coherent biclusters. RESULTS: We present a new rapid method called sem1R that reveals interpretable hidden rules in omics data. These rules capture semantic differences between two classes: a target class as a collection of positive examples and a non-target class containing negative examples. The method is inspired by the CN2 rule learner and introduces a new refinement operator that exploits prior knowledge in the form of ontologies. In our work this knowledge serves to create accurate and interpretable rules. The novel refinement operator uses two reduction procedures: Redundant Generalization and Redundant Non-potential, both of which help to dramatically prune the rule space and consequently, speed-up the entire process of rule induction in comparison with the traditional refinement operator as is presented in CN2. CONCLUSIONS: Efficiency and effectivity of the novel refinement operator were tested on three real different gene expression datasets. Concretely, the Dresden Ovary Dataset, DISC, and m2816 were employed. The experiments show that the ontology-based refinement operator speeds-up the pattern induction drastically. The algorithm is written in C++ and is published as an R package available at http://github.com/fmalinka/sem1r.
- Klíčová slova
- Biclustering, Enrichment analysis, Gene expression, Ontology, Symbolic machine learning, Taxonomy,
- Publikační typ
- časopisecké články MeSH
BACKGROUND: One of the major challenges in the analysis of gene expression data is to identify local patterns composed of genes showing coherent expression across subsets of experimental conditions. Such patterns may provide an understanding of underlying biological processes related to these conditions. This understanding can further be improved by providing concise characterizations of the genes and situations delimiting the pattern. RESULTS: We propose a method called semantic biclustering with the aim to detect interpretable rectangular patterns in binary data matrices. As usual in biclustering, we seek homogeneous submatrices, however, we also require that the included elements can be jointly described in terms of semantic annotations pertaining to both rows (genes) and columns (samples). To find such interpretable biclusters, we explore two strategies. The first endows an existing biclustering algorithm with the semantic ingredients. The other is based on rule and tree learning known from machine learning. CONCLUSIONS: The two alternatives are tested in experiments with two Drosophila melanogaster gene expression datasets. Both strategies are shown to detect sets of compact biclusters with semantic descriptions that also remain largely valid for unseen (testing) data. This desirable generalization aspect is more emphasized in the strategy stemming from conventional biclustering although this is traded off by the complexity of the descriptions (number of ontology terms employed), which, on the other hand, is lower for the alternative strategy.
- Klíčová slova
- Biclustering, Enrichment analysis, Gene expression, Ontology, Symbolic machine learning,
- MeSH
- anotace sekvence MeSH
- data mining metody MeSH
- Drosophila melanogaster genetika MeSH
- sémantika * MeSH
- shluková analýza MeSH
- stanovení celkové genové exprese * MeSH
- strojové učení MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
MOTIVATION: The problem of model inference is of fundamental importance to systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only. RESULTS: We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network's topology and the update logic (obtained through, e.g. a biological knowledge base or a literature search), as well as further assumptions about the properties of the network's transitions (e.g. the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an 'initial' sketch, which is extended by adding restrictions representing experimental data to a 'data-informed' sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data. AVAILABILITY AND IMPLEMENTATION: All software and data are freely available as a reproducible artefact at https://doi.org/10.5281/zenodo.7688740.
Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations. In this paper we attempt to follow a big picture view while also providing a particular theory and its implementation to present a novel, purposely simple, and interpretable hierarchical architecture. This architecture incorporates the unsupervised learning of a model of the environment, learning the influence of one's own actions, model-based reinforcement learning, hierarchical planning, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations which are increasingly more abstract, but can retain details when needed. We demonstrate the universality of the architecture by testing it on a series of diverse environments ranging from audio/visual compression to discrete and continuous action spaces, to learning disentangled representations.
In order to improve the h-index in terms of its accuracy and sensitivity to the form of the citation distribution, we propose the new bibliometric index [symbol in text]. The basic idea is to define, for any author with a given number of citations, an "ideal" citation distribution which represents a benchmark in terms of number of papers and number of citations per publication, and to obtain an index which increases its value when the real citation distribution approaches its ideal form. The method is very general because the ideal distribution can be defined differently according to the main objective of the index. In this paper we propose to define it by a "squared-form" distribution: this is consistent with many popular bibliometric indices, which reach their maximum value when the distribution is basically a "square". This approach generally rewards the more regular and reliable researchers, and it seems to be especially suitable for dealing with common situations such as applications for academic positions. To show the advantages of the [symbol in text]-index some mathematical properties are proved and an application to real data is proposed.
BACKGROUND AND PURPOSE: While impaired cognitive performance is common in multiple sclerosis (MS), it has been largely underdiagnosed. Here a magnetic resonance imaging (MRI) screening algorithm is proposed to identify patients at highest risk of cognitive impairment. The objective was to examine whether assessment of lesion burden together with whole brain atrophy on MRI improves our ability to identify cognitively impaired MS patients. METHODS: Of the 1253 patients enrolled in the study, 1052 patients with all cognitive, volumetric MRI and clinical data available were included in the analysis. Brain MRI and neuropsychological assessment with the Brief International Cognitive Assessment for Multiple Sclerosis were performed. Multivariable logistic regression and individual prediction analysis were used to investigate the associations between MRI markers and cognitive impairment. The results of the primary analysis were validated at two subsequent time points (months 12 and 24). RESULTS: The prevalence of cognitive impairment was greater in patients with low brain parenchymal fraction (BPF) (<0.85) and high T2 lesion volume (T2-LV) (>3.5 ml) than in patients with high BPF (>0.85) and low T2-LV (<3.5 ml), with an odds ratio (OR) of 6.5 (95% CI 4.4-9.5). Low BPF together with high T2-LV identified in 270 (25.7%) patients predicted cognitive impairment with 83% specificity, 82% negative predictive value, 51% sensitivity and 75% overall accuracy. The risk of confirmed cognitive decline over the follow-up was greater in patients with high T2-LV (OR 2.1; 95% CI 1.1-3.8) and low BPF (OR 2.6; 95% CI 1.4-4.7). CONCLUSIONS: The integrated MRI assessment of lesion burden and brain atrophy may improve the stratification of MS patients who may benefit from cognitive assessment.
- Klíčová slova
- Brief International Cognitive Assessment for Multiple Sclerosis, MRI, brain atrophy, cognition, lesions, multiple sclerosis,
- MeSH
- atrofie diagnostické zobrazování patologie MeSH
- dospělí MeSH
- kognitivní dysfunkce diagnostické zobrazování patologie psychologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- mozek diagnostické zobrazování patologie MeSH
- neuropsychologické testy MeSH
- roztroušená skleróza diagnostické zobrazování patologie psychologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
