The evolution of logic circuits for the purpose of protein contact map prediction.

Samuel D Chapman, Christoph Adami, Claus O Wilke, Dukka B Kc

Open access · goldAbstract read

In one paragraph

Article in PeerJ, 2017. 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

0.7field-weighted citation impact, top 32% 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

1 citing paper in PubMed, 8 citations in OpenAlex.

Evolving autonomous learning in cognitive networks.Scientific reports · 2017
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 3 institutions in 1 country.

Samuel D ChapmanDepartment of Comptuational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA.

Christoph AdamiDepartment of Microbiology and Molecular Genetics and Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA.

Claus O WilkeDepartment of Integrative Biology, The University of Texas at Austin, Austin, TX, USA.

Dukka B KcDepartment of Comptuational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA.

North Carolina Agricultural and Technical State University · USMichigan State University · USThe University of Texas at Austin · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting protein structure from sequence remains a major open problem in protein biochemistry. One component of predicting complete structures is the prediction of inter-residue contact patterns (contact maps). Here, we discuss protein contact map prediction by machine learning. We describe a novel method for contact map prediction that uses the evolution of logic circuits. These logic circuits operate on feature data and output whether or not two amino acids in a protein are in contact or not. We show that such a method is feasible, and in addition that evolution allows the logic circuits to be trained on the dataset in an unbiased manner so that it can be used in both contact map prediction and the selection of relevant features in a dataset.

Indexed as

Evolutionary computationFeature selectionMachine learningMarkov networksProtein contact map prediction

Identifiers

PMID28439455

DOI10.7717/peerj.3139

PMCPMC5398280

OpenAlexW2468262548

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.