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High-throughput biotechnologies are playing an increasingly important role in biomolecular research. Their ability to provide genome wide views of molecular mechanisms occurring in living cells could play a crucial role in the elucidation of biomolecular processes at system level but dataset produced using these techniques are often high-dimensional and very noisy making their analysis challenging because the need to extract relevant information from a sea of noise. Gene function prediction is a central problem in modern bioinformatics and recent works pointed out that gene function prediction performances can be improved by integrating heterogeneous biomolecular data sources. In this contribution we compared performances achievable in gene function prediction by early and late data fusion methods. Given that, among the available late...
We introduce a new concept of nonparametric test for statistically deciding if a model fits a sample of data well. The employed statistic is the empirical cumulative distribution (e.c.d.f.) of the measure of the blocks determined by the ordered sample. For any distribution law underlying the data this statistic is distributed around a Beta cumulative distribution law (c.d.f.) so that the shift between the two curves is the statistic at the basis of the test. Its distribution is computed through a new bootstrap procedure from a population of free parameters of the model that are \emph{compatible} with the sampled data according to the model. Closing the loop, we may expect that if the model fits the data well the Beta c.d.f. constitutes a template for the block e.c.d.f.s that are compatible with the observed data. In the paper we sho...
We consider a homeostatic mechanism to maintain a plastic layer of a feedforward neural network reac- tive to a long sequence of signals, with neither falling in a fixed point of the state space nor undergoing in overfitting. The homeostatsis is achieved without asking the neural network to be able to pursue an offset through local feedbacks. Rather, each neuron evolves monotonically in the direction increasing its own parameter, while a global feedback emerges from volume transmission of a homostatic signal. Namely: 1) each neuron is triggered to increase its own param- eter in order to exceed the mean value of all of the other neurons’ parameters, and 2) a global feedback on the population emerges from the composition of the single neurons behavior paired with a reasonable rule through which surrounding neurons in the same layer are ...
To get a true hybrid framework for taking operational decisions from data, we extend the Algorithmic Inference approach to the Granular Computing paradigm. The key idea is that whether or not we need to make decisions instead of mere computations depends on the fact that collected data are not sufficiently definite; rather, they are representative of whole sets of data that could be virtually observed, and we need to manage this indeterminacy. The distinguishing feature is that we face indeterminacy exactly where it affects the quality of the decision. This gives rise to a family of inference algorithms which can be tailored to many specific decisional problems that are generally solved only in approximate ways. In the paper we discuss the bases of the paradigm and provide some examples of its implementation.
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