Evidence map›Paper›PMID 18366620›Full record

ArticleBMC genomics2008

Investigation of transmembrane proteins using a computational approach.

Jack Y Yang, Mary Qu Yang, A Keith Dunker, Youping Deng, Xudong Huang

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

11 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. A decade and a half of protein intrinsic disorder: biology still waits for physics.Protein science : a publication of the Protein Society · 2013
    Review
  5. Article
  6. The use of classification trees for bioinformatics.Wiley interdisciplinary reviews. Data mining and knowledge discovery · 2011
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
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 at 5 institutions in 1 country.

Jack Y YangDepartment of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. jyang@bwh.harvard.edu
Mary Qu Yang
A Keith Dunker
Youping Deng
Xudong Huang
Brigham and Women's Hospital · USHarvard University · USIndiana University – Purdue University Indianapolis · USNational Institutes of Health · USUniversity of Southern Mississippi · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn important subfamily of membrane proteins are the transmembrane alpha-helical proteins, in which the membrane-spanning regions are made up of alpha-helices. Given the obvious biological and medical significance of these proteins, it is of tremendous practical importance to identify the location of transmembrane segments. The difficulty of inferring the secondary or tertiary structure of transmembrane proteins using experimental techniques has led to a surge of interest in applying techniques from machine learning and bioinformatics to infer secondary structure from primary structure in these proteins. We are therefore interested in determining which physicochemical properties are most useful for discriminating transmembrane segments from non-transmembrane segments in transmembrane proteins, and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins, and in using the results of these investigations to develop classifiers to identify transmembrane segments in transmembrane proteins.

resultsWe determined that the most useful properties for discriminating transmembrane segments from non-transmembrane segments and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins were hydropathy, polarity, and flexibility, and used the results of this analysis to construct classifiers to discriminate transmembrane segments from non-transmembrane segments using four classification techniques: two variants of the Self-Organizing Global Ranking algorithm, a decision tree algorithm, and a support vector machine algorithm. All four techniques exhibited good performance, with out-of-sample accuracies of approximately 75%.

conclusionsSeveral interesting observations emerged from our study: intrinsically unstructured segments and transmembrane segments tend to have opposite properties; transmembrane proteins appear to be much richer in intrinsically unstructured segments than other proteins; and, in approximately 70% of transmembrane proteins that contain intrinsically unstructured segments, the intrinsically unstructured segments are close to transmembrane segments.

Indexed as

AlgorithmsProtein Structure, SecondaryProtein Structure, TertiaryArtificial IntelligenceComputational BiologyMembrane ProteinsMembrane Proteins

Identifiers

PMID18366620
PMCPMC2386072
OpenAlexW2171092071

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.