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<i title="Cold Spring Harbor Laboratory">
<span class="release-stage" >pre-print</span>
AbstractIt is common to consider that a data-intensive strategy is a bias-free way to develop systemic approaches in biology and physiology. At the same time, it is well known that choices concerning collection, sampling, standardization, visualization, and interpretation of data affect the result of its analysis. And seldom a less systemic and more cognitive approach is accepted, according to which organisms' sense and try to predict their trajectories in their environment, which is an<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/595785">doi:10.1101/595785</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zufprmma7bekdkeneq4h4vveum">fatcat:zufprmma7bekdkeneq4h4vveum</a> </span>
more »... c bias in the sampled data generated by the organism's, limiting the accuracy or even the possibility to define robust systemic models. For this reason, we introduce a theory and methodology to assess this inherent incompleteness in the sampled data, based on the definition of homology groups. We test the use of this methodology in two examples, one from systems biology considering a population of cells with chemotaxis, and other from medicine to analyze heart response to exercise.
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