ArticleProceedings of the National Academy of Sciences of the United States of America2016
Global informatics and physical property selection in protein sequences.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed.
- Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.Proteins · 2024Article
- Dynamic and conformational switching in proteins.Biopolymers · 2021Article
- The structure of protein dynamic space.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.PloS one · 2020Article
- Sequence-specific dynamic information in proteins.Proteins · 2019Article
- ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins.Protein science : a publication of the Protein Society · 2019Article
- Sequence-, structure-, and dynamics-based comparisons of structurally homologous CheY-like proteins.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The degree of informatic independence between the physical properties of amino acids as encoded in actual protein sequences is calculated. It is shown that no physical property can be identified that carries significantly less information than others and that the information overlap between different properties and different length scales along the sequence is essentially zero. These observations suggest that bioinformatic models based on arbitrarily selected sets of physical properties are inherently deficient.
Indexed as
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Registered trials
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.