Evidence map›Paper›PMID 30626996›Full record

ArticleEvolutionary bioinformatics online2018

NommPred: Prediction of Mitochondrial and Mitochondrion-Related Organelle Proteins of Nonmodel Organisms.

Keitaro Kume, Toshiyuki Amagasa, Tetsuo Hashimoto, Hiroyuki Kitagawa

Open access · goldAbstract read
In one paragraph

Article in Evolutionary bioinformatics online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.0field-weighted citation impact, top 23% 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

15 citing papers in PubMed, 26 citations in OpenAlex.

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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 1 institution in 1 country.

Keitaro KumeGraduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan.
Toshiyuki AmagasaGraduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan.
Tetsuo HashimotoGraduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan.
Hiroyuki KitagawaGraduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan.
University of Tsukuba · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To estimate the functions of mitochondria of diverse eukaryotic nonmodel organisms in which the mitochondrial proteomes are not available, it is necessary to predict the protein sequence features of the mitochondrial proteins computationally. Various prediction methods that are trained using the proteins of model organisms belonging particularly to animals, plants, and fungi exist. However, such methods may not be suitable for predicting the proteins derived from nonmodel organisms because the sequence features of the mitochondrial proteins of diversified nonmodel organisms can differ from those of model organisms that are present only in restricted parts of the tree of eukaryotes. Here, we proposed NommPred, which predicts the mitochondrial proteins of nonmodel organisms that are widely distributed over eukaryotes. We used a gradient boosting machine to develop 2 predictors-one for predicting the proteins of mitochondria and the other for predicting the proteins of mitochondrion-related organelles that are highly reduced mitochondria. The performance of both predictors was found to be better than that of the best method available.

Indexed as

gradient boosting machinemachine learningmitochondrial proteinsmitochondrion-related organelle proteinsnonmodel organisms

Identifiers

PMID30626996
PMCPMC6305954
OpenAlexW2906118506

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.