Evidence map›Paper›PMID 37649578›Full record

ArticleBiophysical reports2023

Revealing gene regulation-based neural network computing in bacteria.

Samitha S Somathilaka, Sasitharan Balasubramaniam, Daniel P Martins, Xu Li

Open access · goldAbstract read
In one paragraph

Article in Biophysical reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
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

4 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Can bacteria think?EMBO reports · 2025
    Article
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

4 authors at 2 institutions in 2 countries.

Samitha S SomathilakaVistaMilk Research Centre, Walton Institute for Information and Communication Systems Science, South East Technological University, Waterford, Ireland.
Sasitharan BalasubramaniamSchool of Computing, University of Nebraska-Lincoln, Lincoln, Nebraska.
Daniel P MartinsVistaMilk Research Centre, Walton Institute for Information and Communication Systems Science, South East Technological University, Waterford, Ireland.
Xu LiDepartment of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska.
University of Nebraska–Lincoln · USSouth East Technological University · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacteria are known to interpret a range of external molecular signals that are crucial for sensing environmental conditions and adapting their behaviors accordingly. These external signals are processed through a multitude of signaling transduction networks that include the gene regulatory network (GRN). From close observation, the GRN resembles and exhibits structural and functional properties that are similar to artificial neural networks. An in-depth analysis of gene expression dynamics further provides a new viewpoint of characterizing the inherited computing properties underlying the GRN of bacteria despite being non-neuronal organisms. In this study, we introduce a model to quantify the gene-to-gene interaction dynamics that can be embedded in the GRN as weights, converting a GRN to gene regulatory neural network (GRNN). Focusing on

Identifiers

PMID37649578
PMCPMC10462848
OpenAlexW4385581601

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.