438 research outputs found
Programmable models of growth and mutation of cancer-cell populations
In this paper we propose a systematic approach to construct mathematical
models describing populations of cancer-cells at different stages of disease
development. The methodology we propose is based on stochastic Concurrent
Constraint Programming, a flexible stochastic modelling language. The
methodology is tested on (and partially motivated by) the study of prostate
cancer. In particular, we prove how our method is suitable to systematically
reconstruct different mathematical models of prostate cancer growth - together
with interactions with different kinds of hormone therapy - at different levels
of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104
Hamming-like distances for ill-defined strings in linguistic classification
Ill-defined strings often occur in soft sciences, e.g. in
linguistics or in biology. In this paper we consider l-length strings which have in each position one of the three symbols 0 or false, 1 or true, b or irrelevant. We tackle some generalisations of the usual Hamming distance between binary crisp strings which were recently used in computational linguistics. We comment on their metric properties, since these should guide the selection of the clustering algorithm to be used for language classification.
The concluding section is devoted to future work, and the string approach, as currently pursued, is compared to alternative approaches
Stochastic concurrent constraint programming and differential equations
We tackle the problem of relating models of systems (mainly biological systems) based on stochastic process algebras (SPA) with models based on differential equations. We define a syntactic procedure that translates programs written in stochastic Concurrent Constraint Programming (sCCP) into a set of Ordinary Differential Equations (ODE), and also the inverse procedure translating ODE's into sCCP programs. For the class of biochemical reactions, we show that the translation is correct w.r.t. the intended rate semantics of the models. Finally, we show that the translation does not generally preserve the dynamical behavior, giving a list of open research problems in this direction
Mean-Field Limits Beyond Ordinary Differential Equations
16th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM 2016, Bertinoro, Italy, June 20-24, 2016, Advanced LecturesInternational audienceWe study the limiting behaviour of stochastic models of populations of interacting agents, as the number of agents goes to infinity. Classical mean-field results have established that this limiting behaviour is described by an ordinary differential equation (ODE) under two conditions: (1) that the dynamics is smooth; and (2) that the population is composed of a finite number of homogeneous sub-populations, each containing a large number of agents. This paper reviews recent work showing what happens if these conditions do not hold. In these cases, it is still possible to exhibit a limiting regime at the price of replacing the ODE by a more complex dynamical system. In the case of non-smooth or uncertain dynamics, the limiting regime is given by a differential inclusion. In the case of multiple population scales, the ODE is replaced by a stochastic hybrid automaton
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks
We consider the problem of the stability of saliency-based explanations of Neural Network predictions under adversarial attacks in a classification task. Saliency interpretations of deterministic Neural Networks are remarkably brittle even when the attacks fail, i.e. for attacks that do not change the classification label. We empirically show that interpretations provided by Bayesian Neural Networks are considerably more stable under adversarial perturbations of the inputs and even under direct attacks to the explanations. By leveraging recent results, we also provide a theoretical explanation of this result in terms of the geometry of the data manifold. Additionally, we discuss the stability of the interpretations of high level representations of the inputs in the internal layers of a Network. Our results demonstrate that Bayesian methods, in addition to being more robust to adversarial attacks, have the potential to provide more stable and interpretable assessments of Neural Network predictions
Polarity assessment of reflection seismic data: a Deep Learning approach
We propose a procedure for the polarity assessment in reflection seismic data based on a Neural Network approach. The algorithm is based on a fully 1D approach, which does not require any input besides the seismic data since the necessary parameters are all automatically estimated. An added benefit is that the prediction has an associated probability, which automatically quantifies the reliability of the results. We tested the proposed procedure on synthetic and real reflection seismic data sets. The algorithm is able to correctly extract the seismic horizons also in case of complex conditions, such as along the flanks of salt domes, and is able to track polarity inversions
Dysferlinopathy course and sportive activity: clues for possible treatment
LGMD2B is a frequent proximo-distal myopathy with rapid evolution after age 20. Exacerbating factors may be physical exercise and inflammation. There is very little information about the effect of sportive activity in LGMD2B, since eccentric exercise frequently results in muscle damage. LGMD2B has often an onset with myalgia and MRI imaging (STIR-sequences) shows myoedema. In a prolonged observational study of a series of 18 MM/LGMD2B patients we have studied the pattern of clinical and radiological evolution. The disease has an abrupt onset in the second decade and most patients perform sports before definite disease onset. On the basis of Gardner-Medwin and Walton scale, grade 4 is reached two years faster in patients who performed sports (over 1000 hours). Other considerations regarding pathogenetic mechanism and response to treatment show a poor response to immunosuppressive treatment of muscle inflammation. Preventing a strenuous physical activity should be recommended in patients with high CK and diagnosed or suspected to have dysferlin deficiency
Experimental Biological Protocols with Formal Semantics
Both experimental and computational biology is becoming increasingly
automated. Laboratory experiments are now performed automatically on
high-throughput machinery, while computational models are synthesized or
inferred automatically from data. However, integration between automated tasks
in the process of biological discovery is still lacking, largely due to
incompatible or missing formal representations. While theories are expressed
formally as computational models, existing languages for encoding and
automating experimental protocols often lack formal semantics. This makes it
challenging to extract novel understanding by identifying when theory and
experimental evidence disagree due to errors in the models or the protocols
used to validate them. To address this, we formalize the syntax of a core
protocol language, which provides a unified description for the models of
biochemical systems being experimented on, together with the discrete events
representing the liquid-handling steps of biological protocols. We present both
a deterministic and a stochastic semantics to this language, both defined in
terms of hybrid processes. In particular, the stochastic semantics captures
uncertainties in equipment tolerances, making it a suitable tool for both
experimental and computational biologists. We illustrate how the proposed
protocol language can be used for automated verification and synthesis of
laboratory experiments on case studies from the fields of chemistry and
molecular programming
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