Evidence map›Paper›PMID 42056703›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Tracing Molecular Chronologies Onto Growing Biological Networks.

M Fayez Aziz, Gustavo Caetano-Anollés

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

2 authors.

M Fayez AzizDepartment of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. aziz4@illinois.edu.
Gustavo Caetano-AnollésDepartment of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. gca@illinois.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronologies derived from phylogenetic reconstruction enable the tracing of evolutionary history onto networks that model the evolution of biological systems. Here, we outline protocols for constructing and analyzing time series of evolving biological networks, integrating molecular chronologies with dynamic network models to explore the evolution of structural and functional complexity. The methodology employs Pajek for network visualization and R packages for statistical tests to examine connectivity patterns, enabling the study of preferential attachment, degree distributions, and the interplay between long-term evolutionary trends and short-term adaptive dynamics. These approaches provide a framework for uncovering fundamental principles of molecular evolution across deep timescales.

Indexed as

Computational BiologyEvolution, MolecularAlgorithmsPhylogenySoftwareBipartite networksHierarchical modularityNetwork growthPajekPHPPhylogeneticsProtein domainProtein loopRSCOPSUPERFAMILYTree of Life

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.