Evidence map›Paper›PMID 31893338›Full record

ArticleJournal of computer-aided molecular design2020

Influence of feature rankers in the construction of molecular activity prediction models.

Gonzalo Cerruela-García, José Pérez-Parra Toledano, Aída de Haro-García, Nicolás García-Pedrajas

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Article in Journal of computer-aided molecular design, 2020. 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

4 authors.

Gonzalo Cerruela-GarcíaDepartment of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain. gcerruela@uco.es.
José Pérez-Parra ToledanoDepartment of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain.
Aída de Haro-GarcíaDepartment of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain.
Nicolás García-PedrajasDepartment of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the construction of activity prediction models, the use of feature ranking methods is a useful mechanism for extracting information for ranking features in terms of their significance to develop predictive models. This paper studies the influence of feature rankers in the construction of molecular activity prediction models; for this purpose, a comparative study of fourteen rankings methods for feature selection was conducted. The activity prediction models were constructed using four well-known classifiers and a wide collection of datasets. The ranking algorithms were compared considering the performance of these classifiers using different metrics and the consistency of the ranked features.

Indexed as

Models, MolecularSoftwareAlgorithmsHumansFeature rankingMolecular activity predictionQSAR

Identifiers

PMID31893338

What OpenQuestion holds

Textmetadata
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.