| Title |
Topology driven modeling: the IS metaphor
|
|---|---|
| Published in |
Natural Computing, June 2014
|
| DOI | 10.1007/s11047-014-9436-7 |
| Pubmed ID | |
| Authors |
Emanuela Merelli, Marco Pettini, Mario Rasetti |
| Abstract |
In order to define a new method for analyzing the immune system within the realm of Big Data, we bear on the metaphor provided by an extension of Parisi's model, based on a mean field approach. The novelty is the multilinearity of the couplings in the configurational variables. This peculiarity allows us to compare the partition function [Formula: see text] with a particular functor of topological field theory-the generating function of the Betti numbers of the state manifold of the system-which contains the same global information of the system configurations and of the data set representing them. The comparison between the Betti numbers of the model and the real Betti numbers obtained from the topological analysis of phenomenological data, is expected to discover hidden n-ary relations among idiotypes and anti-idiotypes. The data topological analysis will select global features, reducible neither to a mere subgraph nor to a metric or vector space. How the immune system reacts, how it evolves, how it responds to stimuli is the result of an interaction that took place among many entities constrained in specific configurations which are relational. Within this metaphor, the proposed method turns out to be a global topological application of the S[B] paradigm for modeling complex systems. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 5% |
| Unknown | 20 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 29% |
| Student > Master | 3 | 14% |
| Researcher | 3 | 14% |
| Student > Doctoral Student | 2 | 10% |
| Professor | 2 | 10% |
| Other | 3 | 14% |
| Unknown | 2 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 38% |
| Agricultural and Biological Sciences | 2 | 10% |
| Physics and Astronomy | 2 | 10% |
| Mathematics | 1 | 5% |
| Biochemistry, Genetics and Molecular Biology | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 4 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 November 2022.
All research outputs
#6,412,648
of 23,206,358 outputs
Outputs from Natural Computing
#25
of 145 outputs
Outputs of similar age
#60,323
of 229,045 outputs
Outputs of similar age from Natural Computing
#2
of 12 outputs
Altmetric has tracked 23,206,358 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 145 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 82% of its peers.
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We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.