A segmented principal component analysis -- regression approach to QSAR study of peptides
DOI10.1016/J.JTBI.2012.03.028zbMath1397.92193OpenAlexW1996048108WikidataQ44389506 ScholiaQ44389506MaRDI QIDQ1784758
Bahram Hemmateenejad, Ramin Miri, Maryam Elyasi
Publication date: 27 September 2018
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2012.03.028
amino aciddipeptidepartial least squaresegmented partial least squaressegmented principal component regression
Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Protein sequences, DNA sequences (92D20)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design
- Some remarks on protein attribute prediction and pseudo amino acid composition
- Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
- SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition
- Prediction of GABA\(_{\mathrm A}\) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine
- Prediction of protein submitochondria locations based on data fusion of various features of sequences
This page was built for publication: A segmented principal component analysis -- regression approach to QSAR study of peptides
