A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

DOI10.1016/j.jtbi.2009.07.031zbMath1403.92088OpenAlexW2087137877WikidataQ84346859 ScholiaQ84346859MaRDI QIDQ1628916

Lourdes Santana, Eugenio Uriarte, Santiago Vilar, Humberto González Díaz

Publication date: 11 December 2018

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2009.07.031

zbMATH Keywords

Markov chainsQSARcolorectal cancerelectrostatic potentialsequence alignmentcomplex networkslinear discriminant analysisbiomarkersprotein sequenceHP lattice

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40)

Related Items (5)

A novel descriptor of protein sequences and its application ⋮ Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition ⋮ Some remarks on protein attribute prediction and pseudo amino acid composition ⋮ Characterization of structure-antioxidant activity relationship of peptides in free radical systems using QSAR models: key sequence positions and their amino acid properties ⋮ A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets

Uses Software

• STATISTICA
• Cell-PLoc
• ProtIdent
• QuatIdent
• GPCR-CA
• FoldRate

Cites Work

• Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices
• Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition
• Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach
• The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
• Predicting protein structural class based on multi-features fusion
• Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices

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