Evidence map›Paper›PMID 42178476›Full record

ArticleAdvances in experimental medicine and biology2026

Behavior Prediction in Signaling Networks: Modeling of p16-Mediated Pathway.

Rza Bashirov

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Article in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Rza BashirovDepartment of Mathematics, Eastern Mediterranean University, Famagusta, North Cyprus, Mersin-10, Turkey. rza.bashirov@emu.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative analysis of signaling networks necessitates modeling frameworks that explicitly account for both the intrinsic randomness of biochemical reactions and the pervasive uncertainty associated with incompletely characterized kinetic parameters. In this chapter, a comprehensive hierarchical Petri net model of the p16-mediated signaling pathway in higher eukaryotes is developed and examined under deterministic, purely stochastic, and fuzzy stochastic modeling paradigms. The proposed framework integrates hierarchical Petri nets with stochastic firing semantics and incorporates parameter uncertainty through fuzzy kinetic rates represented by

Indexed as

Cyclin-Dependent Kinase Inhibitor p16Models, BiologicalSignal TransductionAnimalsComputer SimulationFuzzy LogicHumansKineticsPrediction AlgorithmsStochastic ProcessesCyclin-Dependent Kinase Inhibitor p16Fuzzy setp16-mediated signaling pathwayQuantitative modelingSimulation-based behavior predictionStochastic simulation

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.