Evidence map›Paper›PMID 30120369›Full record

ArticleScientific reports2018

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems.

Enrique Barreiro, Cristian R Munteanu, Maykel Cruz-Monteagudo, Alejandro Pazos, Humbert González-Díaz

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Enrique BarreiroDepartment of Computation, Computer Science Faculty, University of A Coruna (UDC), 15071, A Coruña, Spain.
Cristian R MunteanuDepartment of Computation, Computer Science Faculty, University of A Coruna (UDC), 15071, A Coruña, Spain.
Maykel Cruz-MonteagudoCenter for Computational Science (CCS), University of Miami (UM), Miami, 33136, FL, USA.
Alejandro PazosBiomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña, 15006, Spain.
Humbert González-DíazFaculty of Science and Technology, University of the Basque Country (UPV/EHU), 48940, Biscay, Spain. humberto.gonzalezdiaz@ehu.es.ORCID 0000-0002-9392-2797

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Sh

Identifiers

PMID30120369
PMCPMC6098100

What OpenQuestion holds

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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.