ArticleJournal of theoretical biology2018
A novel model for protein sequence similarity analysis based on spectral radius.
Article in Journal of theoretical biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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
2 citing papers in PubMed, 11 citations in OpenAlex.
- Use of 2D FFT and DTW in Protein Sequence Comparison.The protein journal · 2024Article
- Classification of Protein Sequences by a Novel Alignment-Free Method on Bacterial and Virus Families.Genes · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in sequencing technologies led to rapid increase in the number and diversity of biological sequences, which facilitated development in the sequence research. In this paper, we present a new method for analyzing protein sequence similarity. We calculated the spectral radii of 20 amino acids (AAs) and put forward a novel 2-D graphical representation of protein sequences. To characterize protein sequences numerically, three groups of features were extracted and related to statistical, dynamics measurements and fluctuation complexity of the sequences. With the obtained feature vector, two models utilizing Gaussian Kernel similarity and Cosine similarity were built to measure the similarity between sequences. We applied our method to analyze the similarities/dissimilarities of four data sets. Both proposed models received consistent results with improvements when compared to that obtained by the ClustalW analysis. The novel approach we present in this study may therefore benefit protein research in medical and scientific fields.
Indexed as
Identifiers
What OpenQuestion holds
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.